{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Yellowbrick for Machine Learning Visualizations on Facebook Data\n",
    "\n",
    "Paul Witt\n",
    "\n",
    "The dataset below was provided to the UCI Machine Learning Repository from researchers who used Neural Networks and Decision Trees to predict how many comments a given Facebook post would generate. \n",
    "\n",
    "There are five variants of the dataset. This notebook only uses the first. \n",
    "\n",
    "The full paper can be found here: \n",
    "\n",
    "http://uksim.info/uksim2015/data/8713a015.pdf\n",
    "\n",
    "\n",
    "### The primary purpose of this notebook is to test Yellowbrick. \n",
    "\n",
    "# Attribute Information:\n",
    "\n",
    "\n",
    "All features are integers or float values. \n",
    "\n",
    "\n",
    "1 \n",
    "Page Popularity/likes \n",
    "Decimal Encoding \n",
    "Page feature \n",
    "Defines the popularity or support for the source of the document. \n",
    "\n",
    "\n",
    "2 \n",
    "Page Checkins’s \n",
    "Decimal Encoding \n",
    "Page feature \n",
    "Describes how many individuals so far visited this place. This feature is only associated with the places eg:some institution, place, theater etc. \n",
    "\n",
    "\n",
    "3 \n",
    "Page talking about \n",
    "Decimal Encoding \n",
    "Page feature \n",
    "Defines the daily interest of individuals towards source of the document/ Post. The people who actually come back to the page, after liking the page. This include activities such as comments, likes to a post, shares, etc by visitors to the page.\n",
    "\n",
    "\n",
    "4 \n",
    "Page Category \n",
    "Value Encoding \n",
    "Page feature \n",
    "Defines the category of the source of the document eg: place, institution, brand etc. \n",
    "\n",
    "\n",
    "5 - 29 \n",
    "Derived \n",
    "Decimal Encoding \n",
    "Derived feature \n",
    "These features are aggregated by page, by calculating min, max, average, median and standard deviation of essential features. \n",
    "\n",
    "\n",
    "30 \n",
    "CC1 \n",
    "Decimal Encoding \n",
    "Essential feature \n",
    "The total number of comments before selected base date/time. \n",
    "\n",
    "\n",
    "31 \n",
    "CC2 \n",
    "Decimal Encoding \n",
    "Essential feature \n",
    "The number of comments in last 24 hours, relative to base date/time. \n",
    "\n",
    "\n",
    "32 \n",
    "CC3 \n",
    "Decimal Encoding \n",
    "Essential feature \n",
    "The number of comments in last 48 to last 24 hours relative to base date/time. \n",
    "\n",
    "\n",
    "33 \n",
    "CC4 \n",
    "Decimal Encoding \n",
    "Essential feature \n",
    "The number of comments in the first 24 hours after the publication of post but before base date/time. \n",
    "\n",
    "\n",
    "34 \n",
    "CC5 \n",
    "Decimal Encoding \n",
    "Essential feature \n",
    "The difference between CC2 and CC3. \n",
    "\n",
    "\n",
    "35 \n",
    "Base time \n",
    "Decimal(0-71) Encoding \n",
    "Other feature \n",
    "Selected time in order to simulate the scenario. \n",
    "\n",
    "\n",
    "36 \n",
    "Post length \n",
    "Decimal Encoding \n",
    "Other feature \n",
    "Character count in the post. \n",
    "\n",
    "\n",
    "37 \n",
    "Post Share Count \n",
    "Decimal Encoding \n",
    "Other feature \n",
    "This features counts the no of shares of the post, that how many peoples had shared this post on to their timeline. \n",
    "\n",
    "\n",
    "38 \n",
    "Post Promotion Status \n",
    "Binary Encoding \n",
    "Other feature \n",
    "To reach more people with posts in News Feed, individual promote their post and this features tells that whether the post is promoted(1) or not(0). \n",
    "\n",
    "\n",
    "39 \n",
    "H Local \n",
    "Decimal(0-23) Encoding \n",
    "Other feature \n",
    "This describes the H hrs, for which we have the target variable/ comments received. \n",
    "\n",
    "\n",
    "40-46 \n",
    "Post published weekday \n",
    "Binary Encoding \n",
    "Weekdays feature \n",
    "This represents the day(Sunday...Saturday) on which the post was published. \n",
    "\n",
    "\n",
    "47-53 \n",
    "Base DateTime weekday \n",
    "Binary Encoding \n",
    "Weekdays feature \n",
    "This represents the day(Sunday...Saturday) on selected base Date/Time. \n",
    "\n",
    "54 \n",
    "Target Variable \n",
    "Decimal \n",
    "Target \n",
    "The no of comments in next H hrs(H is given in Feature no 39).\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Data Exploration \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import pickle\n",
    "import requests\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yellowbrick as yb \n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Shape (40948, 54)\n",
      "634995                  int64\n",
      "0                       int64\n",
      "463                     int64\n",
      "1                       int64\n",
      "0.0                   float64\n",
      "806.0                 float64\n",
      "11.291044776119403    float64\n",
      "1.0                   float64\n",
      "70.49513846124168     float64\n",
      "0.0.1                 float64\n",
      "806.0.1               float64\n",
      "7.574626865671642     float64\n",
      "0.0.2                 float64\n",
      "69.435826365571       float64\n",
      "0.0.3                 float64\n",
      "76.0                  float64\n",
      "2.6044776119402986    float64\n",
      "0.0.4                 float64\n",
      "8.50550186882253      float64\n",
      "0.0.5                 float64\n",
      "806.0.2               float64\n",
      "10.649253731343284    float64\n",
      "1.0.1                 float64\n",
      "70.25478763764251     float64\n",
      "-69.0                 float64\n",
      "806.0.3               float64\n",
      "4.970149253731344     float64\n",
      "0.0.6                 float64\n",
      "69.85058043098057     float64\n",
      "0.1                     int64\n",
      "0.2                     int64\n",
      "0.3                     int64\n",
      "0.4                     int64\n",
      "0.5                     int64\n",
      "65                      int64\n",
      "166                     int64\n",
      "2                       int64\n",
      "0.6                     int64\n",
      "24                      int64\n",
      "0.7                     int64\n",
      "0.8                     int64\n",
      "0.9                     int64\n",
      "1.1                     int64\n",
      "0.10                    int64\n",
      "0.11                    int64\n",
      "0.12                    int64\n",
      "0.13                    int64\n",
      "0.14                    int64\n",
      "0.15                    int64\n",
      "0.16                    int64\n",
      "0.17                    int64\n",
      "0.18                    int64\n",
      "1.2                     int64\n",
      "0.19                    int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"/Users/pwitt/Documents/machine-learning/examples/pbwitt/Dataset/Training/Features_Variant_1.csv\")\n",
    "\n",
    "# Fetch the data if required\n",
    "DATA = df\n",
    "print('Data Shape ' + str(df.shape))\n",
    "\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page Popularity/likes</th>\n",
       "      <th>Page Checkins’s</th>\n",
       "      <th>Page talking about</th>\n",
       "      <th>Page Category</th>\n",
       "      <th>Derived5</th>\n",
       "      <th>Derived6</th>\n",
       "      <th>Derived7</th>\n",
       "      <th>Derived8</th>\n",
       "      <th>Derived9</th>\n",
       "      <th>Derived10</th>\n",
       "      <th>...</th>\n",
       "      <th>Post published weekday-Fri</th>\n",
       "      <th>Post published weekday-Sat</th>\n",
       "      <th>Base DateTime weekday-Sun</th>\n",
       "      <th>Base DateTime weekday-Mon</th>\n",
       "      <th>Base DateTime weekday-Tues</th>\n",
       "      <th>Base DateTime weekday-Wed</th>\n",
       "      <th>Base DateTime weekday-Thurs</th>\n",
       "      <th>Base DateTime weekday-Fri</th>\n",
       "      <th>Base DateTime weekday-Sat</th>\n",
       "      <th>Target_Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>11.291045</td>\n",
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       "      <td>...</td>\n",
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       "      <td>11.291045</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.495138</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>634995</td>\n",
       "      <td>0</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>11.291045</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.495138</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <th>4</th>\n",
       "      <td>634995</td>\n",
       "      <td>0</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>11.291045</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.495138</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Page Popularity/likes  Page Checkins’s  Page talking about  \\\n",
       "0                 634995                  0                 463   \n",
       "1                 634995                  0                 463   \n",
       "2                 634995                  0                 463   \n",
       "3                 634995                  0                 463   \n",
       "4                 634995                  0                 463   \n",
       "\n",
       "   Page Category  Derived5  Derived6   Derived7  Derived8   Derived9  \\\n",
       "0              1       0.0     806.0  11.291045       1.0  70.495138   \n",
       "1              1       0.0     806.0  11.291045       1.0  70.495138   \n",
       "2              1       0.0     806.0  11.291045       1.0  70.495138   \n",
       "3              1       0.0     806.0  11.291045       1.0  70.495138   \n",
       "4              1       0.0     806.0  11.291045       1.0  70.495138   \n",
       "\n",
       "   Derived10       ...         Post published weekday-Fri  \\\n",
       "0        0.0       ...                                  0   \n",
       "1        0.0       ...                                  1   \n",
       "2        0.0       ...                                  1   \n",
       "3        0.0       ...                                  0   \n",
       "4        0.0       ...                                  0   \n",
       "\n",
       "   Post published weekday-Sat  Base DateTime weekday-Sun  \\\n",
       "0                           0                          0   \n",
       "1                           0                          0   \n",
       "2                           0                          0   \n",
       "3                           0                          0   \n",
       "4                           0                          0   \n",
       "\n",
       "   Base DateTime weekday-Mon  Base DateTime weekday-Tues  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          1                           0   \n",
       "3                          0                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Base DateTime weekday-Wed  Base DateTime weekday-Thurs  \\\n",
       "0                          0                            0   \n",
       "1                          0                            0   \n",
       "2                          0                            0   \n",
       "3                          1                            0   \n",
       "4                          0                            0   \n",
       "\n",
       "   Base DateTime weekday-Fri  Base DateTime weekday-Sat  Target_Variable  \n",
       "0                          1                          0                0  \n",
       "1                          0                          1                0  \n",
       "2                          0                          0                0  \n",
       "3                          0                          0                0  \n",
       "4                          1                          0                0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FEATURES  = [\n",
    "    \"Page Popularity/likes\",\n",
    "    \"Page Checkins’s\",\n",
    "    \"Page talking about\",  \n",
    "    \"Page Category\",\n",
    "    \"Derived5\", \n",
    "    \"Derived6\", \n",
    "    \"Derived7\", \n",
    "    \"Derived8\", \n",
    "    \"Derived9\", \n",
    "    \"Derived10\", \n",
    "    \"Derived11\", \n",
    "    \"Derived12\", \n",
    "    \"Derived13\", \n",
    "    \"Derived14\", \n",
    "    \"Derived15\", \n",
    "    \"Derived16\", \n",
    "    \"Derived17\", \n",
    "    \"Derived18\", \n",
    "    \"Derived19\", \n",
    "    \"Derived20\", \n",
    "    \"Derived21\", \n",
    "    \"Derived22\", \n",
    "    \"Derived23\", \n",
    "    \"Derived24\", \n",
    "    \"Derived25\", \n",
    "    \"Derived26\", \n",
    "    \"Derived27\", \n",
    "    \"Derived28\", \n",
    "    \"Derived29\",\n",
    "    \"CC1\",\n",
    "    \"CC2\",\n",
    "    \"CC3\",\n",
    "    'CC4',\n",
    "    'CC5',\n",
    "    \"Base time\",\n",
    "    \"Post length\",\n",
    "    \"Post Share Count\",\n",
    "    \"Post Promotion Status\",\n",
    "    \"H Local\",\n",
    "    \"Post published weekday-Sun\",\n",
    "    \"Post published weekday-Mon\",\n",
    "    \"Post published weekday-Tues\",\n",
    "    \"Post published weekday-Weds\",\n",
    "    \"Post published weekday-Thurs\",\n",
    "    \"Post published weekday-Fri\",\n",
    "    \"Post published weekday-Sat\",\n",
    "    \"Base DateTime weekday-Sun\",\n",
    "    \"Base DateTime weekday-Mon\",\n",
    "    \"Base DateTime weekday-Tues\",\n",
    "    \"Base DateTime weekday-Wed\",\n",
    "    \"Base DateTime weekday-Thurs\",\n",
    "    \"Base DateTime weekday-Fri\",\n",
    "    \"Base DateTime weekday-Sat\",\n",
    "    \"Target_Variable\"\n",
    " \n",
    "]\n",
    "\n",
    "# Read the data into a DataFrame\n",
    "df.columns=FEATURES\n",
    "df.head()\n",
    "#Note: Dataset is sorted.  There is variation in the distributions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40948 instances with 54 columns\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Determine the shape of the data\n",
    "print(\"{} instances with {} columns\\n\".format(*df.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Yellowbrick Covariance Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pwitt/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from yellowbrick.features.rankd import Rank2D \n",
    "from yellowbrick.features.radviz import RadViz \n",
    "from yellowbrick.features.pcoords import ParallelCoordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify the features of interest\n",
    "# Used all for testing purposes\n",
    "features = FEATURES\n",
    "\n",
    "# Extract the numpy arrays from the data frame \n",
    "X = df[features].as_matrix()\n",
    "y = df[\"Base time\"].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9e02YMEHR0dFq2rTp9Z4mANRbzZs318aNG20OPrqasl2hyp5TuZKnp6dyc3OV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff6f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer with the Covariance ranking algorithm \n",
    "visualizer = Rank2D(features=features, algorithm='covariance')\n",
    "\n",
    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
    "visualizer.transform(X)             # Transform the data\n",
    "visualizer.show()    # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KaWkpzp8/j3379gGAxufl4eGhduzxxx8HAI3zTbOysjB79mxUVFQgKipKZaqIFJru165d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\nAwAAACFgIA0AAACEgIE0AAAAEIL/H8exnJX2QCxjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100ffef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer with the Pearson ranking algorithm \n",
    "visualizer = Rank2D(features=features, algorithm='pearson')\n",
    "\n",
    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
    "visualizer.transform(X)             # Transform the data\n",
    "visualizer.show()    # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Extraction \n",
    "\n",
    "Create a bunch object to store data on disk. \n",
    "\n",
    "- **data**: array of shape `n_samples` * `n_features`\n",
    "- **target**: array of length `n_samples`\n",
    "- **feature_names**: names of the features\n",
    "- **filenames**: names of the files that were loaded\n",
    "- **DESCR**: contents of the readme\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Features_Variant_1.arff\n",
      "- Features_Variant_1.csv\n",
      "- Features_Variant_2.arff\n",
      "- Features_Variant_2.csv\n",
      "- Features_Variant_3.arff\n",
      "- Features_Variant_3.csv\n",
      "- Features_Variant_4.arff\n",
      "- Features_Variant_4.csv\n",
      "- Features_Variant_5.arff\n",
      "- Features_Variant_5.csv\n",
      "- meta.json\n",
      "- README.md\n",
      "(40949, 53)\n",
      "(40949,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets.base import Bunch\n",
    "DATA_DIR = os.path.abspath(os.path.join(\".\", \"..\", \"pbwitt\",\"data\")) \n",
    "   # Show the contents of the data directory\n",
    "for name in os.listdir(DATA_DIR):\n",
    "    if name.startswith(\".\"): continue\n",
    "    print (\"- {}\".format(name))\n",
    "\n",
    "def load_data(root=DATA_DIR):\n",
    "    filenames     = {\n",
    "        'meta': os.path.join(root, 'meta.json'),\n",
    "        'rdme': os.path.join(root, 'README.md'),\n",
    "        'data': os.path.join(root, 'Features_Variant_1.csv'),\n",
    "        \n",
    "    }\n",
    "    \n",
    "    #Load the meta data from the meta json\n",
    "    with open(filenames['meta'], 'r') as f:\n",
    "        meta = json.load(f)     \n",
    "        feature_names = meta['feature_names']    \n",
    "\n",
    "    # Load the description from the README. \n",
    "    with open(filenames['rdme'], 'r') as f:\n",
    "        DESCR = f.read()\n",
    "\n",
    "\n",
    "    # Load the dataset from the data  file.\n",
    "    dataset = pd.read_csv(filenames['data'], header=None)\n",
    "    #tranform to numpy\n",
    "    data   = dataset.iloc[:,0:53] \n",
    "    target = dataset.iloc[:,-1] \n",
    "    \n",
    "    # Extract the target from the data\n",
    "    data   = np.array(data)   \n",
    "    target = np.array(target)\n",
    "\n",
    "    # Create the bunch object\n",
    "    return Bunch(\n",
    "        data=data,\n",
    "        target=target,\n",
    "        filenames=filenames,\n",
    "      \n",
    "        feature_names=feature_names,\n",
    "        DESCR=DESCR\n",
    "    )\n",
    "\n",
    "# Save the dataset as a variable we can use.\n",
    "dataset = load_data()\n",
    "\n",
    "print(dataset.data.shape)\n",
    "print(dataset.target.shape)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from yellowbrick.regressor import PredictionError, ResidualsPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn import cross_validation\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import ElasticNet, Lasso\n",
    "from sklearn.linear_model import Ridge, Lasso \n",
    "from sklearn.model_selection import KFold\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build and Score Regression Models \n",
    "\n",
    "* Create function -- add parameters for Yellowbrick target visulizations  \n",
    "* Score models using Mean Absolute Error, Mean Squared Error, Median Absolute Error, R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_and_evaluate(dataset, model, label,vis, **kwargs ):\n",
    "    \"\"\"\n",
    "    Because of the Scikit-Learn API, we can create a function to\n",
    "    do all of the fit and evaluate work on our behalf!\n",
    "    \"\"\"\n",
    "    start  = time.time() # Start the clock! \n",
    "    scores = {'Mean Absolute Error:':[], 'Mean Squared Error:':[], 'Median Absolute Error':[], 'R2':[]}\n",
    "    \n",
    "    for train, test in KFold(dataset.data.shape[0], n_folds=12, shuffle=True):\n",
    "        X_train, X_test = dataset.data[train], dataset.data[test]\n",
    "        y_train, y_test = dataset.target[train], dataset.target[test]\n",
    "        \n",
    "        estimator = model(**kwargs)\n",
    "        estimator.fit(X_train, y_train)\n",
    "        \n",
    "      \n",
    "        expected  = y_test\n",
    "        predicted = estimator.predict(X_test)\n",
    "        \n",
    "        #For Visulizer below \n",
    "        if vis=='Ridge_vis':\n",
    "            return [X_train,y_train,X_test,y_test]\n",
    "        if vis=='Lasso_vis':\n",
    "            return [X_train,y_train,X_test,y_test]\n",
    "            \n",
    "    \n",
    "        scores['Mean Absolute Error:'].append(metrics.mean_absolute_error(expected, predicted))\n",
    "        scores['Mean Squared Error:'].append(metrics.mean_squared_error(expected, predicted))\n",
    "        scores['Median Absolute Error'].append(metrics.median_absolute_error(expected, predicted ))\n",
    "        scores['R2'].append(metrics.r2_score(expected, predicted))  \n",
    "\n",
    "    # Report\n",
    "    print(\"Build and Validation of {} took {:0.3f} seconds\".format(label, time.time()-start))\n",
    "    print(\"Validation scores are as follows:\\n\")\n",
    "    print(pd.DataFrame(scores).mean())\n",
    "    \n",
    "    # Write official estimator to disk\n",
    "    estimator = model(**kwargs)\n",
    "    estimator.fit(dataset.data, dataset.target)\n",
    "    \n",
    "    outpath = label.lower().replace(\" \", \"-\") + \".pickle\"\n",
    "    with open(outpath, 'wb') as f:\n",
    "        pickle.dump(estimator, f)\n",
    "\n",
    "    print(\"\\nFitted model written to:\\n{}\".format(os.path.abspath(outpath)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Scores and Visualization Below: \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pwitt/anaconda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build and Validation of Facebook Lasso took 16.674 seconds\n",
      "Validation scores are as follows:\n",
      "\n",
      "Mean Absolute Error:       8.270001\n",
      "Mean Squared Error:      883.090939\n",
      "Median Absolute Error      4.094740\n",
      "R2                         0.318446\n",
      "dtype: float64\n",
      "\n",
      "Fitted model written to:\n",
      "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook-lasso.pickle\n"
     ]
    },
    {
     "data": {
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0ymtWSAQAAABQDxAUXWE75TUrJAIAAACoB0g1LmhQesxhhcSWP37O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb545f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Lasso Scores and Visualization Below: \\n\")\n",
    "fit_and_evaluate(dataset, Lasso, \"Facebook Lasso\",'NA')\n",
    "# Instantiate the linear model and visualizer \n",
    "lasso = Lasso()\n",
    "visualizer = PredictionError(lasso)\n",
    "visualizer.fit(fit_and_evaluate(dataset, Lasso, \"X_train\",'Lasso_vis')[0], fit_and_evaluate(dataset, Lasso, \"y_train\",'Lasso_vis')[1])  # Fit the training data to the visualizer\n",
    "visualizer.score(fit_and_evaluate(dataset, Lasso, \"X_train\",'Lasso_vis')[2], fit_and_evaluate(dataset, Lasso, \"y_train\",'Lasso_vis')[3])\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Scores and Target Visualization Below:\n",
      "\n",
      "Build and Validation of Facebook  Ridge took 0.440 seconds\n",
      "Validation scores are as follows:\n",
      "\n",
      "Mean Absolute Error:       8.367239\n",
      "Mean Squared Error:      881.948637\n",
      "Median Absolute Error      4.169019\n",
      "R2                         0.301782\n",
      "dtype: float64\n",
      "\n",
      "Fitted model written to:\n",
      "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook--ridge.pickle\n"
     ]
    },
    {
     "data": {
      "image/png": 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PHl1qm+vN29XpHP/EKFrx9Vr3enV50XfQzc3tuonZze5Jq9PpaN++PWvXriUqKoq6devy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GEG/Nz3xDMs1kyGhobo7OwEqp8PTdP42Ptunf7/s32F/1SUz51PxiSvZ7nh1HXtVZR5Z3XPHJM2\n1Z/s8XhqGagaNc5RakK0Ro0ar5qTDWRez8BupjtvBZkdu/dRNzTG8jXr0AMvCR6PP0Aur5/0zDu3\nVCAQjqKkSpjBBPmkl1s+euGs177cqJXr33fRvMdsGhamURUe8ZYw3XuH0OoijCUb6Xmxn3vufAJZ\nFnFsd8FznzIhmvobqrNIl29ooa4+wL/d/gh6NESiOcjYaIne3iI9jVcgikUQFUYG9nLNzWtOeA7/\necejXPPOdbP+7a47dtDbcCXywH6ccIL2wUfpaBCo7/SQHs5y713bph2Ij6eUr9C+ZO5we0WVsS2X\nfY1vRUmPUvnec/zhZ+aOwLnrH55ir9OB5doI5SKSrOGqAnI5j2Aa0NiF6PEhGCV8/gCaY5LvP4Q9\negi5OI7rOngUl+VNURTdJG2KjD37OwzNh1fXiS5fi+ILYJsmyaPPEFm0geRkFjHegq84gWVZ2PkM\nFuBUKiCpmI6L46tDUKqmXa7jUJE9HBnupaVtjGzPAWzVzyHvJr7690/T1qRQ3+gjNWEyllaQm67m\nqiuvZN8zT1LpO4C3NEFucoSgWSRi5UgmhzErFexKGXBBEKt/XBfBrODICq7jYHsjlIwCPknBUHzk\nnRzf/uXv+MgN8PAzu+d1rz4XgvxTUT53Mgtsp2MM1WvB61luON2fLEPGtqf/fcqpd2pM2vHPg1oG\nqkaNc4+aEK1Ro8Yp4WQCmderBOz4gfYaEMnZTMoBduzYwcaNG2eJ0RXXfIB7v/WP3PyxC+aInAe+\ns4cv/903gGog1OABVxTxeTSKuQwdzdocATtz1IrrOCRawgwemWAymSfREuaB/3iGD37qjfMaDWUn\ni1z59jU89os9eP0afr/A9XUpxvvLvO0PrkFR5ZfNuJZLJrIqTZsRHX9sel2Y3+VXIe6rgKxixzzg\nDeO4IE0Ocjhj8s6Yb/ocpmZ9To1hGRkt8uhvB0jsm2BsIE3vkE1PyxtxAnVI4/1sGP0FW/73ZQsa\nEx1/vPP1fgJEmqIwEaYpvXOOCIWqWN3yvzZx218/zsC4ghlbBLKM4w1V7zGjjNq9A2XV5VAUaKiv\nJxiO0OsPY9guiuAgjPUQ61qGJxRkZWcHiiQy9NxW+kfGMEIJnHwKd3KIqCqw4YYb2bl7LxM5cMKN\nqKqEPdCN0b4GoVLAUn3g8eOkR0iNj1KxLZoaG5g4coB0roRlVPjZgw9hJjpRvFHsYAujXRcxUswi\n7dlG5wWX4FvfTnjiMIFgiA2drXz6XdczNDTEP//kAZ6frJATPYjhegRPCbGUx/FFjpkmqQi2hWCV\nkUe6sePtCICj+SjmC/gkgaDoYCcW8T//4dusesONaE3V8Rjw+rlXv568GvGy0ALbNdduPq1jqF4r\nXg+xN92ffO993Pv0Vqy6xYi4BGWTlauWLDiftUaNGucW0uc///nPn+6DqAFjY2PYtj09yLnG2cvo\n6CgADQ0Np/lITg+KoqDr+izXxSl0XWf94lYG9j/PxOgwRiGHMzlMm1Dgj99+7SkL4L73iwfJJ5bO\nEruRgJ+BwUHcWBuF3gM0trYDVRGspvr50/d8gPt/+BhHXhhiYiTPi88mOfJMjg+956Ns2nTJ9H4S\nIR/3/td9ZCQv5tARLuoo07ksNucYfAEPKy5sZWwgTaIlzOG9QwQjXq56x1qO7h/hyAsj9B4YZXwo\ny94ne3jhmX6uescaNl+/gp9+63E8usr7/+INrN7Yzv6ne3j3n14xLcJijUEe/M9nWbquGUkSp99z\nSuxtfstKfvWDZ7nqHWvwBWYvBpiGxf0PTTDZeilOvB0nVI8y0o0TrkeeGMBafAGZ0GKGH/wVm69b\nwqqN7TQvjuEPeVi8qoHtW4fZ3/AWjmgrODAeoCfrJSfXYcfbQNXpGH6UT3zmojmCUZJElq5r5jc/\n3sWKC1unj+V7X32Ua9+1fs5xTrH90T6O0soGfz8bNjXPu40kifQfGOKwdwPIMq4vglhIIzg2gmOD\n5kVKDyOLIuFIBBGXobEkvkQzou7HZ5cILVpJRfLQ399PUyyKKgokgl661l1EcyzMokWLCEai7N63\nn97D3eQHjmCVchh9h6iEGpFEATlYh+W4IKsIZhkx2og72kN6ZJBcbDFGIA7pMcTllyL4IxiZCQzL\nxBJkbEnD8gQwx/qJWDk2XHQxkqxQV0zyYv8gW18cZG/3UQ4WRSzNjzh2BMsbwlU9iIUMrmMjFnPI\nA/sQ8ymstjWgqAiyggRIjo13YA8bL7qQxx56kB69iYxh0z+aZDKVIhLwo6oqBGMM7H+eC1evONHH\n64zg9fqenTLI2byyi01dzbzporUsaWvmm794mHxiKUqkHiUYQQ7HySpBtm97gvWLW8+rzF2pVCKb\nzSIIwhyzoxOh6zqXXrCO6zasYGzv0wRFm/b6GFJu/JQ/D2q8fpzv8c+5xPDwMJIkUV+/sPniq6WW\nEa1Ro8brymtdAnYiY6KZ9v8T40myPQfQBGdW+fA3L5g9866np2f69VOlvs8f7sPu38GqxHNE2jyk\nh/LAshMeTzFfoWlRFP+xEllNV4g1BHnLBy6umu5kSnPcYOONId78vmq573zmRDMzrh5dQfdrTIxk\nmRwvgOrhX/9lHzplzIo17cALU2WsO+iLXglWBcEsg2NhRVuQBw7gug7YNtgOz8Vu4Pa//hXtrR7q\nm0KMDmXoGVfobXwLTiAGtomt6gj+CO7AAaT0KI6q05FwFzQmsiyHX/5gF6m0Qe+ghWVKhGPz97ya\nhkVvXwGxqUJ928KBfaI5jHxwpGpIZBm4ioZoFBBVH0I5j5Uew5VVxg/vRzYK+BKLEASQB/YTXbIK\nqI6OIBTnhcM9rA64NMdDjEnVc3lu+1b29Y2Ss1xcPY4d0RAiDbjeLHY4gWgUEUQRwXVwLBMHAcM0\nsXNprMYlaKoXoZDFlRQEWcF1XZxgDDGTxClmEDw+JLOMbRgYig9HkCgefIa8beNvWIHktah0j6D7\nfXh8YayUH230CHnZiykqCJUi0sBBjFWXI3v8yKNHEWwL/FFUs4jiVGiP+tmz/wCTvgRCuB7JF0KS\nZbKuy/Z9L3LpsbmRr6V79dnKzEzhv//8gdM2hupMYmb7w++bFW5ubuYDb7uOSqVCV1dXrf+zRo3z\njJoQrVGjxmnhtSoBW2jswlR2IxuQ+Pg1F9Dc3Dwn6Jk5826KqVLfnBame/cv2fLZTSdtTFQsVPjR\nP2/lxg9tRFYkfv7t7SxZV83sef3atEicSaIlTKVkApBPlwjH/bOcYmeOdinmK2y9by9HD6c5InRh\nhRtot58j0RrgwM5BJkazZCaKoGqMFHR6226oCklJxrXtqqGQ6+I6Fq4vjJxNYvsjuIbDoFNPX6oO\nx2nENctIWqn6WlkBpXrcrmlAOYcgywh6O/X1/gV/P+GmGPc868VMLII2D9LkEHfdsYMtn9w4T+/n\nkwwKLTiaj9Gh/IL7HR0pYPsiiLlJXFFAyiZxHQdXEsEbQg6EiWoSkcZWJkom9uFd+IoTxJdvQPG9\nNM9TEAQmSxWaEzrvveFNfPG7P+LZkRxDnnqKURXHFwXXxU6Po44eqV4rxYOpeBCyKZC16sxStzpn\n1PaGsWUPZqmAWpjA1o4tClg2SDKioqGqKkp+DD0UQJJMpEqO0ad+zfplnaQbq4KnUshjiTIuMoIk\nocSbkP1+lHyGtCVS8oWwZRV1fBB30VqcYBw8PnTHwB9uRT3yLKq3jkJsMe7wEILrIIri9DlPCfAL\nV694zdyrzwUWcuCG134M1ZnC8e0PU/y+5d2aptUyoDVqnIfUhGiNGjXOKU5m7IKGNa8IPRFTM0j3\n/8f/4V1/suYVGRMpmoxjO2z9r73c/CebufrmdTz98ME5wnImk2O5aZOhTKrA7u1HSSfzhON+0sn8\n9IzNeFMIr19jcqJMd/Bi3ECU9ZVn2PL/XjlX1N2+lb7YJhxZg0qxKpRcG2QNVw+CqkMgBraBYJaR\nSlmMZZuRJvpxYi0A2MUscs/zuB4frsePUJhEHn4RK5jAaViCNHaUUSO14LUcG0iDFAJ/FKFSwNVD\n7FSv4bYvPkZHk0KiXmdsYJLeIYu+0HoEewj16HP0SOaCgr93oAIdflxVR5ocxuq8GCE3ge0LIwrg\nVvIEjRGW1fnoLQmIzTeQee4RZM9LLs2O42CZBgwc4vr3/hHRaJTmSIAXTB+lwSSmCShlZFHA7/NS\nbFmF1LsLIdaOK4pYto3o9+JYFkolD4IL5TxiuYDo2kiyRFDWKBolHMMAUca1TZRCga4lS9B0H3Zv\nmSs2b8Ye6Wa4LOI7lnVTPB5kx0JwrZfOW5RpbmtHGxtlZHwCOz8BloVnz2+w/RHUUAyfIqKNp2lr\niFFRfUi6F8oFFH+4eg8cQxAE0hUL0zTPmbEkJ8srMRw61fNFZ5JOp0kmk8Tj8TmLYWcax89lnuJ8\nywrXqFHj1VETojVq1DinONVjFyqVCj2ZMq5cJOAvLWhMZFsOTR1RJsfz5NMlLrp6CXue6gEEykWD\nX3z3SXBguC/Fo/fuJpKYKyxNwyI5lCGfLXPPXU9gGQ5/9JfXndD4Jxzz0T2hI7gOLckdbPmbTfMb\n+nzmCm77P89yNHErGCUQJUA8lrmrzqF0cHERkJL9mEs3IaaGEcxKNcsnybiqjqUHkAf205Z6mo4m\nmYbLPIyM9NLTN0B/aB2jvakFBeNIzzhCcFH1d1HXAuUCUmqIo6vex1HLRBzPIBYGsZZ0IJZzOOEG\npJFuhkdGuOv2rWz5zBVzRfYdOxiQO0AQkdIjWC3H+hsFofo+rguOS7+vhcrO56hbug7NFyDS3EFg\n9ACThstYroxRyKFKUCc7PLj9WW7WdZKWyJplixkv21AwEQNBBEGoGmxls1hGGcwKqB4cUUK2Lfxm\nHjHRgm1buEYRQfeih6J4zQKe7DAF2wIEwEWwTQQRJElGAGJ+Dx6/n5Qj4EoSU0XLsqoR1WXGSg4V\n1wVBwBVEJEWluX0RsVgd49uP0rrhYjasWUU+m+Xhp3dR9scwx46SDsSYHBtHnkhTHu5BDsbp6x/A\nIwnE6uuRVQ1HlDEqFbrOkbEkL8fvU1r6WswXPXy4m6987YtUhAz+qEo+ZaAR5lOf+CydnV2v6Jxe\nD86ErPC54lZco8b5Tk2I1qhR45zjVLrzlsvVILWSnSQanz/gmSqT/fUPnyNaH2DDFZ3TPZ/de4d5\n18evID1e4KEfPYdZtvjo394wr7C8/MZV/O7nuxFlkUfufR6PV+Xtf3rJvMJyasbmaEamt/nNyEd2\n0tGiLNif2dEgcNS2wBuuilFFq/5tlhEcB0GUcSQVIRBFsEwEs4zVtga5bw9W+1oQJUSjyDp3F1v+\natOcc/jWV55ETwRPmCH+2be3E0qEsKQI8sB+7Fg7rqrheINQyiLYNmKyBzvcgFjK4kpyNVPri2I1\nLWdPOc9tf/8MHS0aiXovYyMFekagt+4KEPLIh5/BaeysjjExK7iCAJIClQKCx4fj0Um6Os7YKA2B\nCKIvRHNTHS8+sR1D9kKwDss2MApj9Cpx/uEHP8fQwwQVBVUSkXCnxa0oSQSDQQp1DaiDeynEFiHm\nU3glB1+8CUGSEPtfQIyEcMMBHK8XO1umKOsIyV5ILAFJQlQ94AswNDRIq5Nh+eVXAKCJLmDP+h0u\nX7OO1ONb6U9bEG6YLq91bRst2cN1F6yks9nP0b797OweIKZ6MEb24alrxHYcMsP9OEsuYtHFi0j3\nH8FqWUFZEBkcHKS5uRnBqiANHeLW97/jJD8dZy+/b2npqV7oOny4m8/9/Se55oPLUNSXZgubhsXn\nvvBJvvC5O844MfpaZoVfjlPRl1qjxumktogym5oQrVGjxjnHqRzQ7vFUX6vXtdKfLC24bT5Tpn1p\nYlqEzjQaijeF0H0a7/r4FScUlt/661+ix+tIT2Z518evYNsvX1hQWJqWwE7/lUgDB7HrWqlvtubd\ndor6uIY4kcHx+KqCynVBlFCOPIfjOlApIpUyiIUsjijjShKuqmMnFiH3Po8rq6bIKw0AACAASURB\nVLRN7GDL/zN/1vWd713BgV1DrNnUNmfsS7lkcvVNa9n19BA7emRcUUIsTOAWBBBlcEwEScGuX4yr\n6riaDxwLcXIEVxQRBAFzySb6B/fTm/MjZB0cbxBJTSLkUohmBVf1IqbHcDQfAgKuKOIaRVA0hGPu\nwq43iKMHSfYdISZZPLr9RcpdGxFEqXo9skn0JSt55tlnWb9mNYd37mBj50qiXpXxfHk6GwlVMerX\nZBTFjzJ6kJILrlGgPLCfUDDA4uWrWNaxmZ07n6MXkVIug69+Eb5IGevICzjFHHIwgs/Oo0vgDyjo\ngRCObdMVCwLMEjx6IMTmy69gzzNPcaT7KWwErIyfCBWuWb+Mt199GQ8/s5tDuw4i+sIouNQv7qJr\n2Qr2795JatXFGP44edugYckqUj0HqbgijqwxvnMrGxu8fPrjf3ZeBPSvprT0VC50feXrXzomQud+\nnq75wDK+8vUv8c2vfecVnNlrz2uRFT4ZTnVfao0arye1RZT5qQnRGjVqnJOcKndeTdOqGRDdSy6v\nL1h2OjyQmeV+m0+X8AY0xgbSSLI4x/12JooqE2lr4NHiGq7u2kulaBKOL2z8U9cURtm+C2PpZlzN\ny+jIvgW3Hx0tIeaGEcoFXNdBKOURrApW/WKkiX7sYAxBVnE1P3J2DCvSiFDJg+Ngta2GUp4Oj3rC\ncwjH/UyMZGYZKR3vCjw6dAixXMDsWFst+S3mUHp2Yi67tJq9dN2q2Y8gACKOHkAdPFA1Sxo5DJUi\nSm4cM1iPnBrATHQiWmXMYAIUDWn0KIgyDtXiV2QNRKH634KAYJawlXriXhVn/+MUG9cilvJgW9Uy\n1ebmaplqy0qOdL+IYFaolMus7OxgIruPoUwSQnEQBKxMEjM5hLNiE50dS/EpAnk1BIKAOtrN+hVL\nq+Jx82b0HU+yZ2A/uAaCVSGhCdj+BPH2LrRACFFRKQ4cxCgVEUe6p8XM8YJHD4S46MprWHt4Jx+8\n5lIikQiRSIRiscg//PA+nKZlFBsKeEJxHNtm3KiQeuopHFmlvqWFwcFBSpoPSfdRv+oiHNPANspg\nJPnSn3/0vAiIXm1p6dRC1w/u/w2HU3kEPYCG9YoXutLpNBXSKGp83p8rqkyFNOl0+ozqGT3VWeGT\npdaXWuNspbaIcmJqQrRGjRrnNKfCnXcqA7Limg/wkzu/wa3HGRaZhsW3v7qdfP4lQ53kUIaHf7oL\n26qWVyYHM8SbQwu+T6I5iHhEob45iD+sk06+nFNskcq6W0AUQVLp6WFhQ5/ePPbSVqRkPwLuscyo\nH6VvN2brarAMBEUBQcDs2lgtya1fDK6LkB5BKBeobzlxlsPr18hMFKeP4XhXYNOw6O0v4TT4qwIR\nt1p661ItpxWlqgC1zWrfZaVYfWF+HNEVsPQAdqwFN5tEtC3MttWI+RROvKMqXmUNARc3UIeYGcMJ\n1yNmRnFCCSRZAsdGciwcUYLhg6xYsQLBU4/kDSBK8qwAV5Ak0obLqkWLMY88j3fZRVyxYRXP7T3A\ni70vUJhIYkwMIi7diN8oEIjWsay9hV1HBhBCcWhdxYE9z7Nh85VoXj9rG8N0NlyGt3ExyDK614dp\nmrxwuId0MYMpyjilArGJbj48IyiZyux3j2epOAKa6NIVC3Lr+2+eFbhMjRQxKxUMyyHT30fZdnEl\nGcfSsIb66GxZRnNzM6N9RzEnXURVQ3QsoppMx7q150VfKLz60tKpzMZgyUbw+HALaZrjoVec2Ugm\nk/ij6oLb+KMq4+PjZ5QQhVObFT4ZphYPJK9FpZCvmnepL323nC9uxTXOTmqLKCemJkRr1KhR42WY\nWeprrb2B/+//PkQwaBGsUxkdytPTX0KXDD70Z5fzs29vZ9XGdn738z20L01MGxI5LqQnCgu+z9hQ\nDioFRgcyeP0a5dLCTrE94zKCmkOwTHAs+vyrTmzo89Un6a2/AnlwP1bTSgSzhCsIuKoH21+HnOrH\n9tcdMxDKIxYz1R7NXKo6b1SUcMtFxvoXdsVF93HXl59gy6cvm3sMX9lGX8MVuJ7AtOgULAMr0oDc\nvw+rbQ1MPahtE1wHaegQgiBita7ClWSkXAqxkMEJRsG1cafKjI/1gtrxNuShF7F9YRBEHM2LkhkB\nRcMd7sbS/VQOPYsnJKOG48gVF+lYQOuYBo5RRlQ9iIqKo3jQBIdPvuet/PDBR/jN3sOkSxbu+Ai+\neBuBgJ+GlatQVZWy67LryADrF7dwdGiUdMUimZqk0L2LrliQG999I9+4/zG0aN30NZFlmQtXr8A0\nTSzLwgla/Pf333oCQSjhShLH943C7CA9n04xPDyM2NhZvb6AqOkYo/0M9PfT0tpKQ7yOzSurfYeK\noiDLMpXeF84bp9xXU1p6fGZjSgqN/R6ZjXg8Tj5lLLhNPmUQj8+fMT2dzPxO7B4vUBZEPK5DV8z3\nitsfTobBwUF2vXCA4tEkjuJBNMuEVYHla9ahB6oLfLWxQzXORM4Ec68zmZoQrVGjRo2TYHap7wfJ\nZrN86e4fk4vbSMnf8PH/XRVeazcv4vH79vGRv3rzLCE23Jvip996fGFhOexgL1tFz+EjmIa14GiY\nu776JIPKmqqpj6wiFLMIRond5UVVQ59GifoGH6NjZXoGKvQ2XI1oWZjLLkfIjePoAQSjjOsJgm1g\nta9FProLK9oIioajeZGHu1G7d2A1L0cwykgTfRzV1IVdcQs6fbFN3P5Xv6K9I0B9g5/RoQy9/WX6\n1S6sjg6kycFqCa4g4iIim2XsulbkkW5cUcIVxKr4NQ3EYhpj6WW4gQiI1WytmxpGLKbAtnFFuZpN\nneoBtQzsUBylZxd2NgmqhmQbyI6DGE2gxVsJSzYBucgLh4/ibVtKLp9lsvdQtV/yWJCrCQ5hRaRz\naScej4eJis36K69l15OP41l6Ma5ZZmCgn8HxNK2JKLIsQyjO0aHRaXGZDUv8+U1X0dTUBDCnnNEy\nKpjlMorHg6aqNNf5ZonQmaLHF37JQff4cq6ZQfpwpkC5UEQ4sgetaTGi7keQVWTXwVJ9jA70s6Q+\nPCvgea1KKc9UXk1p6anMbITDYTTCC36eNMKEQgtXUpx+bATbBdF9+U1/D1KpFN9+4DFyscXIkQam\nrnzattmxYwcbN25ED4TOu7FDNc4OTqe519mAeLoPoEaNGjVON6VSiYmJCUqlhc2IoBrERqNROjo6\nWN5ST2G4h+Z6YTqQ3LP9KB/6zDVzAssnf32Ad36smjE1jdmmQtWZo49gmyZiuUBv4xXc9dXthGO+\n6dEwD3z/abY9+AI/+sZWvvyXv2WP0YXRuho71o4TbsRu6MIJJkAQ6PMs5bHMMn7ylMQTfRH6IhdW\n3XGLkwi58ercUM2LUM4hpgbAKIPr4ioa2A441T+iUcRYcgmuJCMYRZxAlN7EZdx1x455z+GuLz9O\nn74ccWKQ3uhFbB1r4Z7tLo8f9dPTfj1W+2rk/n2IYz0I+UmE3ARCKQuWgat6sJqWYkebQRCw420I\nroOd6KyWHts2QjGDUEzjSiKu4kEwywiOVS3thWqWVfGA6sEOxhDNEq6q4zQvw+q8ECvRSXlihGhh\nmNUXXESkLo6RyzK89xmK9UtwmpZCvA2naSmFeCeT/d1cf+mFs8RHQdCQVLWaNTUrCB4vyVQamD2L\nU1EU/DKzMkO3XnsVTt8+CukUO7c9xtZtT7JtzwEee2Ibz/3yx1xz0dpZ13Qh0SMeEz0zg3S3aQmV\naAuezrVUYoso9r+IU8rjlPK45QLl/U+RzOSZKBo8u3c/pVLppVLKa6962Xv/XGLqd+HYszPMC12P\nqczGfOIVZmc2TpZPfeKzPPz9g/N+nh7+/kE+9YnPnvS+Xk+mFkkGA+34utYTXboWX9c6BgPt3PGj\n+0mlZldOvJLv2OP5yUO/Q+tcT0TXcGfOvpUknJaVHNjz/Hm3mFLj7OF0mXudLdQyojVq1DhveTUu\ndqlUisFsGamugQbfMHDMJdc718xnyj23sT3K1Tet5Z5/eQJJlmhaFCUzXqBcMnnrhzcSjvm4645f\nsjN6HXvc1dx2+z46YiaNCT9jg5McPTCGx+8F18FV9Wp5rS0jODbYFk60EVdWkcaOIpQL2M3LsZqW\ng1Gq9kyKMq6sIeaStA8/RkfCpb45yOhwnp79MKB24pYyuHLVNMh1bARBQCqksRu6EHITOPWL2akH\nuO32h+mImSQafIyNlugZgUFnCYKdw1F1nKZluI4D/ghCNok83I3ZshKrbTVCdgx5vBezaRmCZWC3\nrkQe6cZqXFqdHRppRDBKCGYJp3FJtfRW0XBVDzg2YqWIkE9Vx9DYNrgOCBK4VfGMrCB7fNiBGGr/\nXgSrXB1VU0gT8mmEfTH0QIgV69bz8+//K4nL3k46X6JsVnAFERwbTzHFm255Lw9ue5bBko0WlqgU\n8jiKp5qRESUU26RkW5RtB8epjlJxRBnLspBEcU5gHI1G+YNrN/PnX/su6cQSXL3aoxkKBVl+8cX8\n20Pb+GQkQjQaPelyrh/c/1A1SN9/iAnLwhVERFEkGPBRELsodu+qjuRpXUkkGICxHuSCzKTtZ9uv\n7+fmi5bP6kk9X/h9nLVfi8xGZ2cXX/jcHXzl61+iQnrWHNEzcXTLFCebGX61TqEzPwcrOzvYvu9F\nCMURjjlXC5LEZMXGOLyTW99/fvbY1TizOV3mXmcLNSFao0aN85KTcbFbiJ889Dt8yy7iysQELz60\nHai65EYTgTnb5tOlaQfceFMI1aPwxneuo1IyZznKAmz55MXc/vlf0ttwBfbwC5TcPCuuv4jN1y2b\n3m6qNHdXLoztr6v2U4oSyCputAkhNYCreZHykziWAYqK448gWRXEQooN2UfY8tmN8/Zw7jYbsPR6\n5ENPYoUbkcb7sVpXVAWgUTUPcoIJjq58L0ez40iHjmDH22GJD/XQdpBV7I61YJngD1XLb71hbM2H\nPNINjonjq8Mt59EObMX2RXGCdQj5CbS9j1QzmaE4wuAh7Fh79eA0L8gqODYg4NS14FaKyL27MJtX\nIaYGcfxRBMARJeShgxiJDvQjO1EWrcLftAgcGwsJychRlBwso4KieQg2tBC2C6iagIUElklEV1hz\n8SXous7hA30IHh8aoHg8OIUMyWNGQLYepfTCUzhNS6iEA+i6juhUReiJDFsefmY3F1x/C7bjYFnW\ndI8mgKO/FMCfjOipIHNwJInX08jS1kaeOtADzrHRMqJIIBCgVCmgt7fT2t6B6tGxwz4uW7EY17aQ\n5HX4KsPnnQid4pU6a79WmY3Ozi6++bXvkE6nGR8fJx6Pn9HluCe7SDI0NMT//flvXpVT6MzPga7r\nXLpqSdXgq2LhiDKiY+FzDT5yw9Xn7X1c48zn9Tb3OpuoCdEaNWqcl5zMiv6m5Yvnfe3MQCwcS2CY\nIUzDwh/WSY3l5mw/0wF3KjsaivrmbAfVkQ3tTRLB1CM0dSi8/Q/nlvkqqsyWv7iE277wFD2BG3E9\nfgTLRBzvxwnFcUURJ9qEWJhEyE/i+sMgSgiOXZ0DepwInd7npzbzlb/+Jf3BNZjxNsTsBK63Kiax\nKwiuWxWDojT1our7yCquICDkUtAcr2YnhapoxHURHAs5N44dbsAV/YilLKherI61iOUc6CHEch4n\n0gj5SRx/DCGcQXCMqqsuIKRHQFZwRRnBthA0L47r4jn4BFaiHSk9PP1etuJBO/wMQsfq6gxSWUVU\nVGzDoFIWsEQZs1IGFySPj4vXrMR13TnCEEDQA7iFaumtWSmTGuyhtPxKBElGAry+AKXe/YykB2ls\nbCRamaC99JJhy8zh5cD0fSNKEoqizLn3pko7X070lEol9uzZTc4WUHIyolkmKFgEsgZ5I3LMvKmE\nK6k0NzejenRc1yWsyXi8L9173YNZBgcHiUaj52V/Epy8s/ZrndkIh8NnnDvufJxsZviHDzz8qvtp\nvV4vbqlAsVhEURR0XZ9l8KUoCvagRHNz86s+rxo1XitO5Wzzc42aEK1Ro8Z5x8mu6K+vVNA0bc7P\njw/EVl33Yb53xxf54CcvpVw05piPzHTAnZkdPRGNTX42v3ERB58bWHDuaEezwlFFq7qjOhbOsbEl\nruPgKhp2uAG5dzdm+7pq36THT0d8fmOUqX0u6vAznnIQ0qM4k6NYehAqperrY+3VkS6tq6vuto4D\nooxrVFC7d2B7vAhG+ZiLrQwCCJUi0uhRjI4NCI6Jq/lwNC/YJkrfXmjsRO3Zhb34AlxJxnVcxHIW\nxxdGSg1huw5CPo0bShwzOBJwAVdW0IoZPG1duJkJKv447jHRqBYzOJ0bCDc0UywbFFNjeKPxaqmw\nJEM5i6LpOLZFRDSnhefxwhBAw6I5HmLMtjmw53ki66/EOHwAq2VFNdur6oQ7V1Mf9OHr3sa3/+Z/\n0djYSCqV4s4f3jsr6IhJNvmKw9w76iWmSjuj0egJRU+pVGLb7v0EA1EUfxwpVHVVLdg20fJuArKF\nFKvHNSoMZaKoug/XdXEzSVauWlLdRy7DgT3Pk0xNUv75Y/hlasPVT4JaZuPkMsNuqcAwLr5X0U87\nVdb7woH9TGYEJKoLKSs7O9B1HUVRcGyb1vO4rLHG2cOpmm1+rlEzK6pRo8Z5x8mu6JfL8wdbxwdi\n/mic3lIjt//VVtJlmbtv+80c85FLrlvO9778MJpXedn5oKnRLLIsvaxg9emw6OBPeUP+v3h37Ene\nULyf9tHHUUa7q4Lt/2fvPQPjOuh0799pM3Oma9S7bLnIci9xsBOSkAIpkE3Y0MnNpe3ChQuEso29\nyy4bLksghFBeICFLCWx2uVmSQEKJcUh3LDtxl2VLstXLjDS9zynvB1myZUnjmsTl/D5qTj9HM//n\nPP/i9KH5qpBDh5FCPYiJMaqqZ3diJymv8YIgIpoGBX81cqgHJAkhMQ56Ad1XiXzoVeSDLyPGRlF6\ndqAe2obgr0JyuMGuIkVHEOMhSMeQ+tvRyhtAdWO6SpCiIwipCEImgV5SjdKzA6mhFVuoB3G4Gyke\nwpaKYBvpQvNWoHRuxXSXTIhezAkxmoqhHNxCvqYVWyqKu24+5Ss3UNa4AFtDC87qRuwOF0YiAvEx\n0uk08ViMVDRMNhzCLUyIO3Gki2uXNc9oWDPJpMP1vhuvJd+9g0hOx+bxUz5/MVrXThIHd5Ls3kOu\ndz+pg68wv6kJv98/rZGLvbEVZ+187I2tjJUtZOe+/WQSsTmv/7GpnbM11CkUCuzY1wHRIMvWrMVv\nl6cauAiShNi0goCQI0AWsnFs6TBmMow3F2XD0oWoqkomEWNrWxvR0mak6vn4Ghdhb2yds9GMxVEm\nnY3aRC+53nbSg4fI9bZTm+i9aIbSTzrDxf5vqhwT2QTFmHzpMhvH/g8tu/Jt2KPDiO4AcbufLfs6\nL+pGWxbnN5MNDy0ROoHliFpYWFx0nGyt11zpesen6IWHB4hiI+dawmHHPNYkfsv3/+EJmhZXUFHn\nJxxMkE3nueTqRfzqu88BFB3ZEBtPU1bjY19b75zHFxqKERkc52//8aqZtZ5ff449fe0UmtcgyjJa\n9UTNpnLgRUZGinetHB2MkRWrEbU8NkFHL2SRBg+gVzSBlkfMpdFKa8HmQgr1oAjgu+IWcu1bySgK\npGLotS0IkWFAxHR6weYARNDzmLIN0+VHSkXB7ccM1GH3loDLg5ZIIMVG8NcvJKfaKWBHVz3IQwcn\nRtTk0tRHXqGpVqFyjYfR4T30RTKM9unIvnrcgVKkZALyWfTOV4j6K0EUkQpZBLcP0+ZCHDpADBv+\n4b3c8e63A5zQ4QoEAnzkhivZ+cCviY2PMBhJIFc1Um7qlJT4sbk8CLLC7s5XGRoaYvP23bOmJNod\nDnzNy9i/aydrLp8ZPB+f2nlsOlfHaJT9gyHi8ThjwSCBJWto7x1iXk0lOw8NTDVwESSJlClzWXMj\nRn8H7vKVpBsWYTvG2e/YswuzrhVEEb9dnnKEreHqJ4flbJzYGX7PO67m+08+V3Qbxeppjy2dUD0+\n1q9fT8eeXUTzJoZsZ89zT/EXly6/6NMaLSzOdywhamFhcdFxsrVeNpttzm0cG4h1tu8l765AkySU\n/j0sfFMDleUK+9p6WbCiZlpDoq69w6y6fD7//cMX+MuPXz5DRP7k60/jcEjT0nlnE6zP/XYPt3/u\nqtlrPf/2Cr7+f3fS32eDXApJyyMU8mgVTfSEgieYZaqRb2rC1rkV0x1AcJUgj/XRENpCU6OLyjo3\no8Mpeg4ZDBmVSIUshZ2bcVQ0YtdyxHsPIu3ehFbVjNi7C93pRUhGQRARjAKIAlJ4CNFfgaFrmIoD\nRyFLfnwQM53FlGXS4yPo2Ty24EHydUvRKpoQxwZYlXqej/3TLE2W7nmZgz17iJnLEccGyNcuAdco\n2FRMRDRDx967B68isO7SS1jcuhRXZhBVVUmn03z8HVfz5AttRWt3amtrWd1cz66ogVhXh6TYZjw7\nkurkt89uIahJc6Z9L13YzEtPdZDPpLGpR4PwuVI7A4EAt113JXf/4tcsX3MJhq7T1nEIqbKRuGmy\n89AAq+bXcXholHAyjaYbkIhSNt7FHR/4C2BCaBtHBIOWzxHNmyCK01J1J7nYh6ufCidbW3ohcjI1\nb6dbTztb6YTq8bF64xUTs3dzWYxRldtvvn7q+h9bi32x3hMLi/MRS4haWFhclJxMrVd/f/+c608G\nYg8/uYlILA6GhpKJk1+8kdHhP3DdzWvYv72PknL3NOF0+U1Leeax3Vz5FxPzQR2qgr/MTSSUpGt/\niLGCk5UVMoW8xuU3LeWxH2/hlo9umLaNWDiFrlO8frRGojfnQypkMZw+TFECh4s+LuGBu//Ex/7m\nzTMF3d3PM2hrRR7qIN+0CjGXBEFkqa2Lj925cZYuuy/SIS0gV9pIOp1DMAoIDUup6XqCRkaobPUy\nMtTJ4T6FgYqNGN4yBL0ALj/Ex5AEAVsmSjYXg4alqFoBLRVH95SSdSVQwoMog/sxVS+NI8/ysX+Y\no8nS59/EN+7axuiom0L1QvwyZOwOdE8phqEjZ5OULLuUqngf5dU16IbB41v30DUWR1C9UwH0p95+\nJQ6HY1aHS1VValWJ54N5lJKZga6p65TYJXpjGUSXf846UFVVWbWslbLxLkK6jC47MDNxykSNG998\n6axB9CObnsW+YM2UkBQLE26+IAjgK+dAVzcOLYOY1RElG4JemHJAjxcMqVyBfCZFWS5K65FU3eO5\nGIarW8LlzDmRM3y69bTFSidkmx3ZZicd90zt90zGw1hYWLyxWELUwsLiouRk3ugXE6KT23jvjdey\nO5Tm0LNbScs2cPnp6Zco5DWu/stVM4RkeY2Py29ayiM/eIGqhgCyX6Zz7xCjI1m6SzdyeWkPV7x9\n/tR6V91yVLD6ylwMHhpnf3eWDVfWFz22ykoVOeQCzYeQz4DNiVjIIYwPstN/NV//+g6aKqGiUiU4\nFKcnJNNb/5fIQwfRFm+Y6EAb7KF+9AU+9o9zddm9jLvv2k6v4xLMUh9SdJSVI4/z0bveMkO0/vt3\nnmOw/J3gqyEej2P6SinRExSGgkhrrkPQNYxUjHQuSS6XwUREb1yB2P4iRIZpqhSKCu/GKpHhaARb\nlUZ1iZ+gAKgOjGyK+vmN2Gw2kolh0skE7f2jGK5KxMp5qJ6JMRmDus4PfrOZO99905xplm/bsIb/\nfOlnmN5yhGMCa1PXEQfaaVm/nsL4MGa2eA2wW4ZPfOBdjI+P89NHf8cLXYPEsPN4+68pIcc1qxZz\nx61vn3WeqGyz47cJRHUdQZLQ0kkOdnZSt+bNSIqCaJp4SwMEAwumjciYFAyRSIRvP/Y0ruYlcx7f\nhTxc/UznWlrMZC5n+HQ7hZ5s6UQul+P7j206o/EwFhYWbyyWELWwsLhoORu1Xk6nE59dwVdaRi4a\nR0qM01t9FQ98+/d87LPrpoSkYpNw+1TGhuPExlNUNwYwDJP6BeWsuWIBuXSehx/YTtWSWsprfNME\naFV9CaGhKIf3j2L3uBhsuoLRoW1Fjys4kkJQ7UiqG0GxYyq2ifpOhxtTdXE4cBOHtRzKng4KjVch\nVpVgagUMuwsBQBAQvWXMk+TiArBaogcRBJG6wT/z0b/ZMKto/fCn1/Ltb/6O0cW3IAGGnie283ls\nIuQ7d050+c2kMfQCZu1iBNtEp1fNE0AZ6aTy8uKNT6qqXZRmBQyhMDG2JBNHtdspLS9BNM0JF0Zx\n0N07gNzQgjR4EMV+NHg+mfrI2tpa1i1q4PB4N+F0Hg0RGYOA00bL+vWoHh9ieJDagIvgCVIS0+k0\n9/7qSdoSCuKCN2EXJuZ/JnWd33TsY/Anv+IfPvRuTNOc4Q61LF9JW1sbRl0r4Z4DaPVLMQVhWmfc\n2c5nUjAsKHVdlMPVT2Z2sCVczi6T37HhcJhwOExpaSklJSVF1znZ0onfPvfyGY+HsbCweGOxhKiF\nhcVFz7Fv9I+f+3gy6y4oc9Hc0EAseQAkAa2QY5dtHV+/6xWaamRcLpXhniA33b6AS69dPFUvWshr\n/PKeP+Pxq9TML2VBs5vxgTFgwjm99WMbSSdzJGMZVl0+H9Vt55c/3I5RVk/Pwe1Faz37QiLOBQpG\n2kD0lqElwhiyDUErYHgrJmaB6jqGvwpcJYCAqOfB4cYuGIiSDVHPUlFTvHNvZZULIZ7DzKVpqjCn\nHU86mSMZzeD2T1zbUlucyFgvac1EzyTA7iJbvwzZoRJwOUnqgGxDHWgnZ3Oj210g29FUH6MDA0WP\nIxzMUFVZSV5PsrZ+Pt2yQV88xVA6hSkdmT06eJhCRQMBw8BvE5CPqwE+UX2kqqo0eO0cCmcmZqtK\nMuhHuyNPBsizpSRq+Ry5dAopeIjbPnArj2x6lgOaCzFQOpFiewRBkjDrl9IZ6uKRTc9y+83Xz3CH\nZIeTZcuW07V/D9l4FOx+zISAV1WmpdvOdT4X6wiSk5kdbAmXs8vpOtAnNIgNNwAAIABJREFUekZv\nOtIM6UQjuI5/9q2UbAuLcwtLiFpYWFgwe8CkJMa45pKVJ1z3tuuupHPoEXptEHb70N1+chkfPf4q\neqOjNA5t52//8ZpZncIPfP4tPPGzNja8bSJV8r9/9MI0gel023EeI1z7+zPY+p9hWKnmgW++yMe+\ncNnMhkfffZVMy62QyVCaHScaASE0hKDlyCEjanlwOBGMPKbNDpgTI0BECTmXwu/1IAoiuayD0Ei6\n6LmPBrPgEBASYSqrJsR7aCjG80/sRXXa8JW6GOoNExlNUNtchj25lf6IjRH7fPJNqxAEgYLDw0g0\njMPlQRAE0oEG5PF+zEA9OJyIokjPmK2o8B7oGqFq3nNU1vnpffYZhoc0kvVXIngDCLINweVDdHgI\nh4bxkaHlitlHPhSrjwyHwwzGs0RsXsTKqikXM6rrbN26lbVVHm770LunpSTuHxrjQO8AiYKJ2+1m\nSW05Dz/5Jw4Mh4lpbiSnMGM/giQR06BrLA4w5Q7l8nnau3uI5jQMUcaQ3EiuDPNKVDaubp3qfnui\n83mthqtHo1FCoRDl5eX4/f7T2sZrxcnODr7Qa2NfT87EgT7RMzpbpsDxHPvsWynZFhbnJpYQtbCw\nuOiZK2AaGhzkl39uY9myZVMdVmd7kx4IBPjsu2/CjI3z6NanydQvJ58toORz6KJIU61SNL3V5pBJ\nJ3M43XaueMdyHr3/JW79q5nNgX78rZcJll8CzlI4/Cp7zWbu/tdtNFWLVNT6CI5m6A8KpJfehuAp\nxT2yhauvfDOHd28nsnQFjvqFHH76MRKqimZoGHYnRnwM0zQQBWFiSLyZQxJFyOdoamoi+GehqADs\n7c8gNiQRUlGC+SihoRjPPLZ7RoOlQl7jsR9v4a23rMBf5uLH92xhb9iO4Qpg2F0Yko1MKoliGJgO\nD4YoY6dAQZLQ7CoDthYe+NZWPva5S2ds92ffeJr3fuoyqhsnAsrQUIznfrOH+uxTBErLGB3M0t+b\nJFm9HtFTitdpn6oNPZ4TjZRwLV7HxuMEoWho+CpqqC1hKqid6nb7y8dY9ua3Ync4poRiTyLBtq4d\nmFXNqLMfBobiIGeIhMNhrl67nO89+hSvZlTEQDWSQ0ACdMWBceggWb2GQqEwqxCdPJ/jnaCzOYKk\nu7uLb973f8kJMdwBG8lwHjt+vvDpv6e5ecFpbfNsc7Kzg18LIXohuXCnci5n6kAXe0YzmcxJ1ZE6\nnU4rJdvC4hzGEqIWFhYXPcUCpkygnjv/7T5qF7TM+iY9HA7zw1/+F0//+bd4SuCqlXaG+56iZyDL\niGcpuWSCqrVzj4EBcHtVHn3gJd76njWU1/h4yztXcv9df8JX4aOyzs9oMEfvcIGh2uvRCgWE8gaE\nQBVi53aCSz5EMBFB2r0DV2UrmXIRsb8bzAM4jDw9B9pZXl/BC929SA2LqFt9GYd2bEFsuRwcLrSh\nDozYMLrdjRiJUT9/MYmubcQCjbTH4wi2RTz4ra18ZBYB+OC3tzG88B0IvmoQBHp27OK53+6ZIUJh\nQnDf8tENPPGzNm792EY++vkN3H33XgYa3oOQiIDTh2HYKOTzSIqBqTiQB/Yj1S0mH+wl669lb0jm\n7n9to7HWRkW1h+BomtHuEd7/vy6dJkLnEsI/++4r9CnrieZtaPkcsm16b9uTHSmhqiprly2hUCig\naRqKoiDLMqHe9mlC5pFNz2JvXjXrPFGxdjHhngOolbM3nTIyCQ52DnDfYyaC6qG9s5ukYceZCCOq\nHsRCloBNwNNQTTJQRXt3D2uXTW9AZOg65bLBzx///ZxO0JmOIOnu7uJLd93JNbcvRrFVTP29kNf4\n0lfv5KtfuvecEKMn2wDnbDZpupBcuFM9l1NxoE/EbM/oydaROhwOfv74762UbAuLcxRLiFpYWFzU\nFAuYsskE7R0d2KvnU1+zELuiAEffpP/P6zby7Ycf5eDep3jPp1bM7BR7bxsdlZcyMrJ96u/H1k1O\nptymE1ne8T/X88eHX+WqW1ZQXuNj4dpGHtsioOkLMcsakJvymLqGHqhA0DVkm4qg5REEEHxleDa8\nHVtsBCGdoHrZKhAEcgdfIVPTwkuGyWi0E9+Lv8Fd3URjQxPDWx/BYwRpaHBSXtJLcDRDNuOkZvHN\nbAoq5Ps7MRNRzPIGOowVfOuuNhpqbdTV+RnqHaNvXCbR8k4cspt0NoEpKQz4VrJQbyvq/jpUZcr9\nbayAgVwKUVEgMogh20G2QXgQJTqEc+EqBJePmGmiDneiuwL0u6+nx+ZEHM9hSiZXNj01JUIBXnhy\n35xC+I7/vZbv37uXkL6IWHAEX0XVlBg9vj7yeOdnNkdNURSUI89EoVAgqZmEw2Fqa2uLPleKolDq\ndjAuKej5HNJxgriQzzEeHKW0oQ7XgpVo+Rz50ka8tYvQxwZZs6Aej68E2WYjk4jR1tZGxFtOoVCY\nOh5D10kf2E5S13FXLXnNnKBvfudrR0TozOt9zQcX883vfI0f3PfgGe3jbHAqwuVscCG5cKdzLqfi\nQJ+IuVzYk6l1tlKyLSzObSwhamFhcVFTLGDq7TqAUbcEUzCmnC84+ib9Kz/6OcOD7bzrr1bM3in2\nzvV8459folcQGe4Ns+WP+1GdNvzlbqKhJNlMgTe9tYVspoC3xDXNMQyOZNBLFqLXLUUK9WAGahBz\nKQTTxDTB1A0oqcLetxt7y3pESSKazaOW1CDJMqM7t1C1bB2S24ug68hLNpAb76dEEllUG6AkmOFd\nfz2zvvTBb/yYvLIasmm0eSuRFAfUNDNqriEpQd9oF77AavIBG06HimToqA6JdCRIThQJ1JYVvd7+\nMjfJWAan205luR2h6wBGSTViLoMUH8MoqUFQ7NjdPiTPRHdNt10hpyjkB9ox7S5MfzWynseeiVHZ\n4j16L5M5HGrxNOjKEp32aJz9u3eQ1XQ8ikBzTSXzS9285+ZrAbj/vx6d4fzcdPn6WR21TCYzlaar\nDx/iPlNjQZmHq9cun/ZcZZMJMvEIqrcEye5gXk0lI9370UL9iDXNUw2LTF1nfPdLlHrdLFm5auK+\nZLMYigNJEJDKajk0GmZteSUAqsfH+vXr2f3CZtKHDBS3f+qYU14H0epl0xomFbJZFIcD8Sw4QdFo\nlBxRFFv5nNc7R5RoNHpO1Iy+nk2aLqTGSKdzLmfDgT6RC3sytc7j4+NvWEq2xcXNhZSS/1piCVEL\nC4uLmuMDpslgXRAFErqIIEqIen5KhE6iGwadkQyVarqo8GlocPLiaC1P/uIV7vjizPmav7jnaTbe\n0Dq1vENViIVT9PYkwKNNiFBRxjAMRMWOKAoYgoANnYDXRU3TcqID3WQN0MMRHJlxsmGJitbV6ILI\nUF8veQN0QSQ6NgYmZNr/wEe/uGZW8fyRL17OPXfvYaDiUgR/BQaQ1zVsgkgmkwTRQYm3hHKHixKH\nTCyno5kCoqeG4SEIBYsHn9GxJB7fxI/yaDBNoX4pgmJDKqtH1DIoA+2Y8TBy7Tz08Ag2M4+sadQt\nWcaQ0006l0MrqQHFRjoSZGS4a2rbyWgGf3nxLr9lVSq5kSxyWS0r6qo5dGA/Ow8NIriX8c1H/kjn\ngQMsueJtuI5xWQd1nR/+9mnKFZPoMY5aJpNhy96DCP4KRMUgEIjgWrCSQV3nwd8/S8EUiY4MsO+p\nn+JxZykpc9A1lGRkDMTW65G0PLXxw8QSoyQlFUEv4NUz1CsF1lx51VQdq+JwIBYmrqsgCERz2jT3\nU/X4WNXawhffdT2maeJ0OjFNk3996DHskkQmEaNjzy6ieRNDmdiW3yYQ959ZAB4KhXAHTpB2HrAx\nNjZ2TgjR16pJ0/FcSC7c6Z7LmTrQJ+vCnqjW+Y1Iyba4uLmQUvJfDywhamFhcVEzGTB1RcMcbN87\nFawb8XHGozF8ZVn8LnlGI5hCoUDOEPCXKXNseYJAwEZd7wHu+Je3zCr8Pvj5q3niZ20sXlUHgK/M\nxf3/9iym7OGKki4qakKMDifpPWAwVH4pYmUzZBJ4y8pwZgTsvlIqfaXkEjG00ae5auP1PNPRRyia\nIhmPYXpKUSQRp+rAVdtMQYSSErOoeK6vgH7jiEOXTSIEe8kJEgWbHSMdI5SOUNqyhuWLmpFlecot\nfnWfnZ5nXpi1uVE6mSMaSpKIZVHd9olGR6MCii0Gmh0MHY9dxlk/H6lzjOuvuRybQ2X/4T5SagAj\nl0VOhHD7Kkg4VDK5HHpJNb27zan9uf0q0VCy6P0IDcZRKxYRFVV+//Rz1Ky5DLnORk8mCqjEF72Z\n7a+8wvojc0HhqPPDyH6Mvn1Tjlp7dw+CvwIMA3GgnZb166eWtzWvpvPxn5KIdcxwzAt5jYe+9zhy\n+ToWXfuXGP3t3H7NBkpKSjBNk/v+sHVaMyXZZsdvE4jqOoIkYYjyNId+Mqg/dj7jpBOUScTY2taG\nWdeKIElMhvRRXWf7/i0MDQ3R3Nxc9JrNRXl5OclwvugyyXCe8vLZHdM3grPZpGku3sjGSGebMzmX\nM3GgT9WFnavW+fVOyba4uLmQUvJfL8Q3+gAsLCws3miuWbeCrZv/QCQwH6FuMVJlI1LTMvKyneBg\nH/NqKmesoygKiqERHJ59vEloKMav73+R2GiEJfOKp4tO1k0C9HaFcXic/O0/b+ADn7iE625exAf/\neg1/8/erWJ54Hkc2is8u4x3aiy9QSiERQY+F8JNlYamTvYNj5BQ3uUwG/FUIsg1NlImnsgiFDOVe\nN5UNxQfKV1S5EJLhCRE6cphCzWK0moUYZQ3oNYuQ3AGGR4NTYkhVVWRZZsn8BlIlrfzknpco5LVp\n1+GZR3fTuXsQp9vGf//oBf6/r71AqP4qZNWFkEmgmDoZUySc08maEoHR/YijPUQGe2HwIIF4P0vm\n1eOUAC03UU+KSF/9tTzwzYn9Od12spnC1L6Pp5DX6B+XcHm9xAZ7MBasZSyaQBAEwuk84XQBUZYx\n6lrp2LNr2rqiJBHSRD5x8zXUJnpJde1hrP8wDB7EP949TbhOLh8ePcBtc6Rt3/6pNcT6d/Di1m20\nB1P8cct2qqurKSkpmdXBaVm+EnGgHVPXEY3pItTo28dt100fRzPpBHXs2TUlQo9FkCSoXcAftrxS\n9Fkoht/vx46/6PW248fnm6M18BvIZOfg10KAXEgu3Jmcy6QDXZvoJdfbTnrwELnedmoTvUUD8kkX\ndjbhCKfW6AgmBLHRtw9D16f9fa7/HQuL06XYC5TJcgiL6ViOqIWFxUXP5u27ufRtN9PR0080dXQk\nh7eQwta0iMNDozOCJkkUafJIdHUzwwE8tnNrJJike99w0f1P1k0qNomhgSSf/sq1s6fN3rmee/7p\nv6i79BbW3XgDssOJpmlIoog4dIBOp8KgvxxhYBgNAYRjZlSKAoVkDLG2lrGB4rNBg6NpJMWOMdpD\noW4JiBKYJqapo8oCpc1LCHe8woHDvaxbsYxMJsPOPXvp6e5CT6foLMzjW//8ApUBCdHM877PXDmz\ng+29L1PjsTOcySLVNE8dq5BLQ+NS/rS7m6/csYxYJk3J/GWoLheZRIzoM38mOLAfx7w1pAs6hreC\nPfnruPvLf6Sx0Y3b5eShe57h9s9fNbN51HdeIeleSn3tPEYHehFEiWwhh2EYaObENVKYEGnRvDmj\ns64uTzgnf/WeWxkcHCT366fxNrUg22amp2aTCXylUtEXEFVVNuKltcRsKo9ue553Xz9IbW3trA7O\nZC3o/l070VJR8qMu9CJppaqqUquKvJib3QkyTZMS1c5gWj8jZ+4Ln/57vvTVO7nmg4tnXO/NvzjA\nV79072lt93zmQnLhzvRcTseBPtuO8uuVkm1xcXMhpeS/nlhC1MLC4qJm8sfD1eieMZJjwOegvX0f\nkeqmGR1Jjb59/J+/voMP/1uGh76/g9s/uXoqED+2c6vbrzI+Ei96DNGxJA6nwo/veRl/QC0qXlYv\nLePSpfUMjfYRF0QcpsH8Mhc3veNqvpnJEhzuRDFtmOIRcWSaGNkUQmcbGZefoWCYZH+66GzQ/r4U\nQmsjQl87CEcSZ0wDKZugcV49cj7JW6++mu62ZxkX02zt7CUUjWNraKVpZQ2IEqNj44Re+nc++6U5\nOtje+Sbuuetxcq3vRckkEUURWZJwejwIyRFGq5dx53d/hruuGSnvxG+XaW1uYt3atQSfeILE3j9j\nyk502YaLPEbT5fQYJrmojiGl+M5XtlBTLVNe4yM0mqK/J0GyYg1lSy9BUhQMZSLQNQURwzCQBQBz\n6hgNxUEhl50mRI91fgKBAC67PKsIBcjEI5RWFA+myypUIqkEsupGb1jGr/74DHd++ANzpjTanW5W\n1JbwiZtvw+FwnDCof9uGtTy8/VcIpjnVDAkmRKgZC9G6dCF6ePiMAqPm5gV89Uv38s3vfI0c0Wlz\nRM+V0S1vBKeTlnquNjc5G02eTmVM0GvhKL8eKdkWFzcXUkr+64klRC0sLC5qjv/xOHYkh8PtobV1\nKaOde2Z0JJ18k/7et7yJJzvn8527f0dNhYjPJ6FpxpT4SsWz9HeNUchrFPL6jNEthbxGz8Ex9u15\nkaGozI03Vhc9XnfARigYRHP4ESQFxAnxlMlksPnKWF+/GNvLLxI+uId8STVmOokQC6I1r0XARBob\nxihfzb9/awsf/tzMWZsP3bcV59p34uo/RExxII33YyIgGRqL6yqpFHO0Ll2Iqqo4Vq+jtBCl0+vB\nvvhSpGNEWaXPhafBWbwWtcZGXyZOwe7ELgoIooCuSBSSCYS6FhKuCmqddtLuAHFR5LltO/EW4uiq\nB0d1JUoiihYZpenSq3D4J7r15qLjDG57msySmwh7vewdHUUsq6LgSSMnwwCItomGPQYgmAaCIFDi\nnDj2+BHRJhayKPajwcLxzs+JnCK728vQWPFgeiyYQW6d6PorYTKcnbiPZ8vBqa2tZX1zNV25KNHc\nUaffb5en7mHuLKSINjcv4Af3PUg0GmVsbIzy8vJzMh339eRU7uG53tzk9XYUX0tH+Uzn5lpYzMWF\nlJL/emIJUQsLi4uaE/14ONyeGR1Jjw2A7rjlBoK/fJznHO8noTjY9uwfuGHlUUH2wpP7uPIvlvPj\nf/0jjYsqKKmYPrrliZ9vJ6G5aZivsizgIDIcLnq8fb1Rds1z4jBsUy7hoM3Gg79/nrym4a+dz6o3\nXcbhQ92MeUrQMgnyy6/GLGSg/wBZhx3DUUI3G7jva6/SUCVQXeulkDAYHs6z5NqPMzoWxt1QR+/w\nKLrPi0NLcc26S6ioqJjWtMnMxBnRTDKSOk2EAuRiEZrKiweKFdUeiNswXQFypoEpQX7nM7gWrJ5w\n7RxuGhoaONjbTi5Qz+joCGPldShlNrKeciRfJTVLVxPr2A41jUhuP6H9O8iX1FG/aDmKzYYbGxnV\nh6JpxPM6Su9B/BVXYBcM0oaOXQQhMU7r0oUAbNnXiekOUGITptzOuZyfYk6RbbwPr1JW1HkORmXc\nqgvTNPHbZQSHfept+dlwcFRVpaXSj8fTiG4cHUE0eQ/Pdoqo3+8/J7rjniuczD08X5qbvN6O4um6\nsOeqq2xx4XMhpeS/nlhC1MLC4qLmZH88ju1IeiyBQICP3PBmdn3vIaI4QBQI9kWAiU6xhmawr62X\nj/6ft81wH3/5rT+DafDXX1iDL+AC4NEHXioqXnpDIiUrS5HcXuKmyZZ9nWxYuhB780qGnnkc7+K1\ndOzZRdn669AO7iXq8iI4HBj9e9HsDrS6pRimTpnPQ0rcwN7oKOOhXm5ZPY9v/+u7J1wPu0jOFFFj\nw0g1flqbJ4RhOp1GVVUURcHQdaodAj1peWLG5THHqWkaI+kCvtAJalEHo5i5AcxMEnIZCnqeQtUi\njOFeJMWFbWwY2bGKZcuW88wTvyYpuCgkD+Jyu8mPDFDi9aL4WqlY9WY8oU4WNlbw5KspmldfhiCK\nDAfHSCORGh3E8JQieEpJHzqE3rMXn9NJducmmi65jNWtLVNB66Ut89n/3B+Y19RAevBQUefnRE5R\n5LoNc9ZPPvT9HSirbp+WJisGD894W36mDs5kQC81LEVRpzu8Z3t2psXsFLuH59u80dfLUTxVF/Zc\nd5UtLg5ez1nFFwqWELWwsLjoKfbjQX87t/3vDxddv7a2lsWNdby0txPRptI3JlHIaySjGeLRNB/8\n/NWz1kl+4HNv4af/tolcpjD198tvWspjP94yVWM6SSGv8eA9WwjWXgfj41S7vRN1f75y2rt7WLts\nCaV1TaQ72ojkdGwVfsob5pM4eAAzGkSLj2MuvQIRkCV5qmZQcPpQ6i7jxQNb+ayqTnM9Rt68nM/e\n9xMeHQySF20ImKjoNJX7WeEs8NfveTvffnTT1IxLAMMwGAmNY7pL6GsXiorqnmEdVrYi6DqaICLZ\nHZiGSSEeQijk0HMZfvfsS/hLKxjKGJgty1ElkYbKAKIoEhwbZ2jvNiqaFqKHxykNdeIrr0KQFfqD\n4wgOF5LLjku2kU1EyesmOdnBPFueRZV+bvzA/2LbwR56godJHwleF/gc/N2X70RV1ZNyfoo5RYFA\nYFr9pOQ0GR5OMzwmIC29GYcs481FaV26ELvNRu1pvi0v5gJZjVrOXazmJsU5WRf2fHGVLS58rO/b\nU8cSohYWFhc9x/945JAxMwlKszGuf8v6E/54qKpKeHiAmKhi1NcRyjXy0D2PcOMH1+ALFK+T9Je5\niY+nqKidSGksr/Fx1S0reOJnbWi6ib+mjOBIhv6BNMGyNyF7y8iZIsYRB1cQBKI5jUwqiS5I/OWl\ny9n7mxdJxUKIkoTLoSDaBSK+AKY64baZuoZ5pBZSMA0kWSGCnUgkMuV4DA0N8bnv/pz9ho9caARD\nkBHtKqIM/cNdLFzWjMPhYEGZlwMjMcbzOcaiCTIFg1g2jyBoRCo38uC9m/jInetniup72wjOexti\nMkzBWwGCiMCR2aVOP8aOp1DXXYfhLyO4/ffo8y4BxUE6PdH4SZZlaqoq0UsDeIIHaVm5nFsua+V3\nnf9NKBzFlO2k4nE0wwRRBMWFYKYR0zEGxhxUNbfw/7Z10ORz8Km3Xzlr859TCf7ncoqamxfw1S9/\ng58/9jt2H+ojLEVxXNKKxy6xbEETHo/ntN+Wn6wLZDVqOTexmpucHCdyYc83V9niwsb6vj01LCFq\nYWFhwcSPx23XXcnDT26ieyyK4PQRSiTZvG0Xy5YtKypGM5kMvspa8mP9yKJIoayBwU4Pm361g7r5\nZUX3Wzu/lK2bD7JgRe3U38prfNz6sY387v/t48n+FihrwGb0IWWSqP5SzHwGQ9cQJYlCKsFYdwcv\nREYxCnlk00BKx7hk1VoU1cW+fIRxW4BocGSqJ6wAU46oQxIRRRFTUqb2Hw6H+ey3HuBQaQuy3YMM\nmFoeM59DyMQJtCynP9rPI5ue5bbrrmRP18/Z8+rzGAsuwVRMzIIBkoQgO+hKV/ODe/ZSUWpQVqkS\nHE7R05disOrNSJ4yfA4bkeAQpqQg2BxI+SxSqAelohHJWwKGTtbmRjB0TECWFcajcaorJq6rpCik\nTBmpkKampgavnqE3myOlZcF+JM3V0DH0LIV0GllRSVUuQqmoR1EUBnWdH/xmM3e++6bXJFg41q1p\nrF1ORSJGx55dRKI6Lz/fz/rmaloq/af8tvx0XCCrUcu5hdXc5MyxXGWLcxXr+/bksISohYWFBdMD\ne1/FRFCTdvgYOYn0rnQ6jWBzUFVVTQiV8fAwY01vwT/0JN5I8TrJ2FgKj89BOpmb6qQ7STSSR/T4\nkXpepaSiEsFRQlaSEHQNUZIppBKMdO6jULkAsaqCQCGOd/EShJzCq6++yqWXXsrSlat5+aUXUfIp\ncqYJgoAsTAhRM5umvCIwMVNSLEyd38NPbiLqqiCPxOTQD0G2Icg2UN2Mh8aQBegai6OqKvOrAswf\njXKg7UnSNjc6EnI2iUcyERatI5WL0ZHPEx/3YVZWYcRfwQzUIDo9pHMpbL5SvB4PGAYGJsnYCJRU\nYhbygInh9CEaGlomger1ktULGIaBKE6MltFlO9UOkZKSEi5raeSV53bCgvWYWgE9EcFAQE/HEaOj\nGJ4AoWhsqnHPa+2aHO/WqB4fqzdegZbPkUunaCyETmu/lgt0/mM1NzlzLFfZwuL8RnyjD8DCwsLi\njSSTyTA+Ps7DT26aM7AXjwT2c+F0OrGLJjZTo6aqkqYyP/Z0hEP1N9O5P0Ihr826XiGvkc0UKK/x\nk4xlZnw2OJBhnUdn9Zq1VK++nLJFK5D627ELBqIkEe45gFa3BIcsTXR+bW4CYOnCZnRvOft37UT1\n+HjTxsuolAsIwR7MTAJVNLFrWeorAkiShDY2yCX1pYyPjxMOh+kai6PLDkxhlp8IQSCrm2iiTM4Q\niUQi9MVz6A43866+lZZLLqN6/mL869+K7ZIbsBdSJIb7MGoX46qqR9IL2D0enAP7ELQ8smJHxkC2\n2ZFsNuwjXbjrFqCkIwiZJEYmjT42BNkUQiJMIpkimc6Qy+WAiZmYwvgA773xWgDed9N1qNk4St8u\njAMvT8xQjQWRg4cxquYj1CwmksyQzR51oo51Tc4Gk89UOBymJ5adVWTINjsuf4DBtH7K+510gWbb\nLpz987F47bjtuisx+vZN1KMfw1S69nVXvkFHdn5gucoWFuc3F40jmkwm+fa3v82mTZsYHx+noqKC\nd7zjHXzqU5+amhk4yX/8x3/w0EMPMTQ0RENDA5/4xCe48cYbZ2zzZJezsLA49zi2vi6HTNuufZSW\nBWlZvhLVM30G4onSu1RVpdohkOjvZjyvkBk+RL52CbphMLDoVh6650lu//xVM+okH/vxFq66ZQXb\nNh/E41OnffaLe19iwy2fpH7xMjKZDFv2dSLY3VSIeXxGmHRPinQyAfIQ82pKWdWycOrYVFVl4/IW\n9jz3FKmunQiql6svXcO+/QfQ/BXkJQFkCS0SJBIZJ9/fwe8qa9juRMtBAAAgAElEQVT0bw/izkYp\nqN6JmkrVM+u1MyUZsjEkXSUSibD/UB/mwg1IkoTkUKly+egPhhEcTvTaxRhDPdSXlyJIEkLCZP1l\nb2fPS8/RH+8jLyrEUxnIjOOUBPwLlzIYT+Pzuimpraa/rxevTURqXISp62Si4xS0AkMD/dQFvJSq\nMleva6G6emL+qt/vZ+H8eRzoG0KrXoSEgCBCQapF8ZYiiCKCv5KOnn42HNMJ+Wy4JsfXbBYSEToO\n97IiUDvjmTqT/Vou0IWD1dzkzLBcZQuL85uLRoh+6lOfYvv27bz3ve9l4cKF7Ny5k/vvv59Dhw7x\n3e9+d2q5Bx98kG984xvceOONfOhDH2LTpk187nOfQxAEbrjhhlNezsLC4tzj+Po6PZ3GrNeJOr20\ntbWxfv36GcKhWGAfDocZjGexNSxCHBmioDjJSQ6QBfKeagYOq/zmJy/jdNnxl7mJjk3MEb3qlhX4\ny1x07g1R0A8SqFQZH4rjl0u591++x5Z9nXS1t5E3RRwDBwjG0iglFSRyGs5MkEaPh0uuWIfHM1Mw\nqqrKsmXL+fTb1qOqKk6nk3Q6zSObnqVrLEUsl2F3Vwd6waT80rdhc3sByGQzDL7yHLKUQq4vQ5+x\nZTByGZKxMQ7mo9wvy3SMRnAEIpQH/MiyjCzL1FcECIWjpLNZ8rIDMzFGqddN64rFqKrKmsvejNbW\nhla5gEB4FFkwsVc1gWHgOriDksVrGA8GUdIxyldfztjAfrS6JTgDFcjZBBWlfvxGmlUegztuOdrk\nx+l00tpYzaGkhreqHtPQwdRJ9XeTF0XQNFwOO0kNCoXC1IvIM3VNZqvZLBQKJBLSnM/U6e7XcoEu\nLKzmJmeGNTLDwuL85aIQon/60594+eWX+fKXv8z73vc+AN7znvdQUVHB/fffz44dO1i9ejWJRILv\nfe973Hzzzdx9990AvOtd7+L222/nG9/4Btdffz2CIJz0chYWFucmx9fXKYqCaGgIkoRR10rHnl2s\n3njFtHWKBfaPbHoW1+J1XDkvz7a2rbzS0Y1gH0NHQDA0whWXEIpu533vaiWXKeDxqahuO4W8xk/u\n3cp4800EgyGcMQVf0+WkY/3c95+PU1rXRB6ZHe3t+Beu4G2LFyLLMpqmYeg6L296AhljzvOUtCyl\npaVTQa3D4ZgKeH/4H48w0LwEu6dq2veV5FCx+wLkZRVpsINCzRJE51GhqxfyFDpfwT2viWVXXgUm\nOLydZEUb/cEw9RWBKTFaXVFGIZdjeNDDZUuacZccdXdUj4/169fTsWcX49EQK1sWcfhQG0Ihx6Il\n89nfvQMhnqR61RUoLg9lwOCOzWQEGWdJGSMDHWQyY7z3K1+Y5hqpqso8v4PKyioiZoGsYWAKIpKW\nRdLyOBx2nDITjvCROtGz4ZrMVrOpKAolqp2YZ8msz9Tp7reYC1QoFMhlszSpkiVmzjOs5ianh+Uq\nW1icv1wUQnTbtm0IgsCtt05v3HDDDTfwox/9aEqIbt68mWw2OyVWYaKhx/vf/34+//nP8+qrr7J2\n7dqTXs7CwuK1pdj8xGLrHN9lUVEU/HaZuGkiSBLRvImWz019XkwwHLs9VVW5ZN06uodCJLx+dEQE\nWUFw2DmY8PCDH26jvtykokwhNJJiMGoj1vAOkmoAwVuDVFJKuL+dsUSKzJIr6EnEcadC2Fa8hbQo\nsmVfJxuWHk3B9TUvY/+unay5fGYd2YlETn+iQCSVR1ELCIpt2meBeS0Mv/o8/qZFlJhj9PQdJiva\noZDF7O9g6eIlrLvyKlSPDy2fo8znYSgxhuEpIxSOTnW0NU0TKROjuUTF5ph5fxS7g0Wty6hJDvA/\nbr0Rp9OJaZpkMhmGh4f5ws+fJKVlyYxGCfZ04lx+GQ0+D6KpIyp2tHScnz71In9fXT0t2HzPDdfy\n6F0/oKq1GdM0MQwD3bWa/v270csaKG+oRUhFpkTombomxTp3tjY3sWVfJ5GcjpbPIdvsU/dncr+n\n8xwf7wJlMhnau3uIZHIw2IW2qIH7/+vRGaNcLCxOh9N5Rl9PLFfZwuL85KIQop/85Ce59dZbZ3wp\nRSIRAKQjb5T37dsHQGtr67TlWltbMU2TvXv3snbt2pNezsLC4rXhZOcnzsZc9XWTggFfOYbioJCb\nSH08kVA5fnumIGITDAxBRFCOiA5E8FUyUv0B+qIheP6PUL0E0edHS2YRoocRskkMXQPZg13OAAKm\n00d3TxfVtUcEjq+c9u4e1i5bAkw0JXrpqQ7ymTQ29ahbW+yYw+EwP330d7x4oIcRU0Uej2AXDAJN\ni1FcE86nZLMTKK/EM97DopaFNFeUYGYz1HnLGGmppnTlxqntyTY7ZW4HeKsYD42RzqTI2QVkDPx2\nmZaW+VRXi4RHOjEmRdPkCJOcjp5Js765emoUTCAQmHKGVjfXI9YsZMdLzyGv2oiAgCjLUy6ghImt\nedWMDrE1NTXcuq6Fp8fHiRVMTFFGNjQaKsuRMiMIw2mcZh59UKL+LLgmxWo2VVVlw9KF7NoyTKpr\nF4qvbOp5vea6jaf9HB/rAnWMRtnaPYSsuii1S7Rccx2qx1d0lIvFa8O5LthOlTP5rn0jsFxlC4vz\ni4tCiHq9Xrxe74y/P/zwwwiCwJo1awAYHR3F6/Vit08foVBeXg7A8PDwKS1nYWFx9jmd+YnHMld9\n3aRgaO/uITI+QGHcjxYaoUYV+MyHPjDnNo/fnqIo+CtrCfd1oDcsA1ECQ0PLZMjrIPXuA08ZhYYV\nCIptQrAaOmIuhTTWi+4uxdAMtGwGWRLJivapUSWCIBDNaVO1jaqqsmpZK2XjXYR0+YQpaZPXzqhZ\njFJjIuTAsDtJGzr5zn2U1TYSDw6SM0W0RJR4chzJodI0bx4+vx+HHTBnpgK3LF9JW1sb5ZULyIYN\nVtcGCJSVIwoCRt8+7jgiiB/Z9Cz7h8bYdrAXoXYhJV47rc1NqKo64/5Npp92RMY4NDhCtuDGlGQE\nXcMhCZSWlxOwy9jsdnpGZjaSuuOWGwj/6kn06kUYpomiKIyNjWHoOoHUMB+98Spqa2vPimtyoprN\nSVH9xXddj2maU/W6Z/Icw1EX6PsP/Rfyxo3YnW5k21F3W5QktKoF/OzRJ/j4+99lBeivIeebYDsZ\nzvS71sLCwuJEnNdCdGxsrOjnTqdzzpquxx57jKeeeooNGzawfPlyAFKp1Kw/1JOBSjqdPqXlLCws\nzj5nOj+xWH2dqqqsXrKIigqR9910Ld3d3dhstqLB1vHbUxSF8kAJw3mRwkgned3AiI2DaSA7/aDY\nydcuwVTsmFoeQdcwBREDgVz5POSe3eALEB4dpqqpGbTctJmZhjhRI6rnsmTiEZR8mk98+ANTKa3F\nUtIe2fQsRs1idMPAZ5cIFXTygCBK5Evr6d21Ffuqt2CkEuTGR7Etu4JcVRUdsRAb5i9gTBDY+fvH\n2FjdNKPxjiqaHNr9EmnNZEd0mIBY4JpVi7nj3W+fun4ToulXKA03YHc4kOWjP0GziaZr1q3gZ1/5\nDhlPI4K3dGqmacYwGDzczdrLJl4iztZI6vi6sbzsQBvqP+GLhdPhZDt3lhzTpffnj/9+2nNcKBSm\nXjBIpzAHNJPJMJgxcFVMP59J1zmaN8lnUgz/+3+zoMx1Xgujc5ULVbBZs2otLCxea85rIXr55ZfP\n+ZkgCHz84x/nM5/5zIzPNm/ezD/+4z9SUVHB1772tRnrFdvmqS5nYWFx9ihWiwcnHrMyyYm6LL7v\nSODY399/Usd1/PaWL5pP1/DLZBuXwI7NyPOWk9QE9GAfmk1FFAV0rQC6jqk4wDQmhKkooju9yLEQ\nGX8ARAnVyCOY5tS+CuNDvPKrR/B6cpSUO4iPprjzb1/iC5/+e5qbF8x5jENDQzy+dQ+JshSGKKNn\ncxjRMLpaguT2kR/pJVe5EC00TL7/ADQux26IjITGKfP7p1KCj69LzSRibG1rw6xbSmWdiCs9zvJF\nzUiiSHjowNR9S6fTCILAYEbHVeGedmzHiqZcKsnh6C9YXOEjn89y6fV/we+efpZsLj2RupzPojoc\nlDcv4vDQKIFAYM5GUsfXjZ3Mi4XT5VQ6dx77HE/WdkZzGoYoIxoafrtMQsmc1PiV2dKCj96TVgRJ\nQkxEEGubGLTZzmthdK5yIQq2s/Vda2FhYVGM81qI3nXXXUU/P76GE+CJJ57g7/7u7/B4PDzwwANU\nVVVNfeZ0OqcNOZ9k8m9ut/uUljsddu3addrrWpwbaJoGWPfytSAWizESSWC3j8y5TC6SYNu2bfh8\ns89tnOS61kY2b3uRgYyJrjiQCllqVIHrLllJf38//f39p3Qvj9/eKluS3W2/RnME0EUFU88gO1TM\ndBIzHcMQJEy7E7Q8SDKCoSGYAoJNxdRypMeHiUerCZRXket8Bb1uCbmxIfxdj/Huz6yfMZP0i//0\nST7y/k9TV1c363V78HfPMIwbSToSNKoK7jIb+YEetPAQ2USMvCFiqB5klw+714cuCCQMSAwFSYp5\nqgYHKfd52L33FQKdE912uw/sJ1XdAuk0xELMn1dLLBYDIGO6+dDf/Qv+ihrSeQ3JyDOYNlmIa6q0\nIZtMsK99H4XqRURzWXKGQXrM4PnREPEDr/Cmq66hXNEZGjyAJsiYNifpaJDhkR4SvhIqvC5qE2Mc\nOHDghPdIFEU0TXvN/jdP5pmavB8jkQTQy+7Dg+ArR5COzLOWFNIFk4GOLjZv3kx9fX3RfeZyOcJD\n/cSlo0L8wM7tJCoWYsZjmIUcZjZFOOxFkiQMWyn3/eSXvPPaK4ps9dznXPmezeVybOvsR66fe1TO\nUH8/q9raZpTznMucze/aE3Gu3EuLM8e6lxanynktRG+77bZTWv4///M/+cpXvkJJSQk/+clPWLRo\n0bTPq6uricVi02bLAQSDQQAqKytPaTkLC4uzi8MxEdwXQypkT+oNvc/n453XXkEulyObnVjHZrOd\ncL2T395qRt+8iq898TJJWScoAS4PYiaOo7ScRHgcw+UDU8IEBNNElCTk+ChioAY9E4dYiMWL55ON\nhtm57U8Io7v5H/98+TQRCqDYZG74yHIe/sXP+OJnvjTj2DZv24UybwXSvv3T/i7bHZTPX4xtrJfB\ntEK2vJoqVWZc1DCPzexwOInEElNBhmJ3cLDtBVKKm1AkglKwU1fi+f/Zu/Pwtuoz7//vc7Qe2bJl\neUtsBydxVichgYBDQkjoE9KytEx5hrYP5Ud56DZDy9OW2dqZ/i66l7Z0CnTaaYdfaUsp7QC5HvYS\nlrRlJ2bL5qx2SIjtxLEty5ukI+mc8/vDkbBsWZH37X5dF38gHcuy5Mjnc77f+75ZsqAiebKt6zrv\n7N2HemwPGEcoKvXQ1hKi50SYt3Cwds1qXC4Xx+sPEZu7hNOdIXB7UOwhHHkFmPE4oQVreeXV1/B5\nc4nklkJ+KSgKJhA2DfSj79AVb+WjH1yPruuTfqKf7e9U4vf4SFNLXwgdsItGURRUt4fX6g6fNYi6\nXC7KNIVTZ7YFx6NROkI6XfV1RFExHRqucAf10SCVi5bizvXSHLamxOs1E0QiEQyHO+PJlOFwE4lE\nptXrPZaftUIIMZRpHUSH49FHH+Wb3/wmc+bM4Te/+Q0LFiwYdEyi6+2BAwc499xzk7fv378fRVGS\ntaTZHjcSq1evHvHXiqkhcSVQ3svxsevdJpq8xUPW4pW7Q1x44YVj8r1G+16Gw2Febe5G95bwzPbt\nnDZdxPUIsZ7evsY7nadRFAWbS8PSQ9gUBY8nB1dpGWr9SdYX27HrQeobDlFUVU2O9u6gEJrgcNpx\neg0qKyvx+XwpzyG2cz/zKhfQ1nyCoKahDHjt9N5cFMukIDcHX0EePe0tmAMClKn0NX57p/49nP4S\nNl18DaGeLl7bewj7nPko3e2UlJQkT0xffvGv5Lf8lRu+tnbQ6u39P/srJ47ncdFF6zng9hKOmTjz\nfGBZuN1OvHn5mIZBR1cPYctGqHQRC0tKaGtpIWJYWDY7ViyKruXTEQry0ukYtuaGszaHmUr/Nt84\ndIyDx8N4vd5B91mGgc+fh1VQxJIlS856sj9v3jzufOgp1HNWEGxpprOnF3PJOuyqDSsSorxkBaai\ncPz4fmpqarDK5rFo0aJpvT13qryX4XCY7QebcPXbXTWQrgeoqamZduNEJuqzdqzey5nWsXg6mir/\nLsXovfXWWxPyfdQJ+S6TrKGhgdtuu43CwkLuv//+tCEU4NJLL8XpdPL73/8+eZtlWfzhD3+grKyM\nNWvWDOs4IcTYu3brZsz36jANI+X2ZC3e1sEzNSeLpmn4zF5efG47AXsesfxSWHgedLZg2ZyYrhxM\n1Y5ixPAXl1Bq9bDw3LXMz3Xwt5fW8L2/u54FOTZWbriUd5tPU1w2uPt3f/acvs+7hHA4TGNjI7rV\n91G/bNVq1Mb9WANeO8MCZ6SLIp8X1eHEpZhYZt8xVjyKGerCacY5+O4J8BZS4LJhdzrx5HpxWnFs\nNhvKmdEy0Nd4p2PPdm744nlpV29vuOU8OvZsJ9TTTczmJGKYYFkoXa0U9dtRYjejRB1uIrE4qt3B\nnHnncM68CuYW+sDhxFc8h1jxfJzF5bgqq2nyVnLnQ08RCARG/J5NlA+tPx+a6ge9F5ZhoDbuZ9mq\n1ckmTGeTaMxU3n2cw2+8TKywAmI6rniEeSV+7HY7FhAtqaJu11tD1tSK4Us0qhr4eZRwtnm+U9l0\n+awNBALc8+AjfOf+R/nRYy/ynfsf5Z4HH5kWnwNCzHazYkX0P/7jP4hGo1xyySW88847vPPOOyn3\nL126lKVLl+Lz+fjc5z7Hz3/+cwzD4KKLLuKZZ57hnXfe4a677kpun8r2OCHE2BvYDfVsI0smSyAQ\n4JcPPMijT/w3BSV2ls7N5fTJHo41GzTlrcJx9E0MfwWKqqAqCrHWo5QtWkqJW2VRxXwqek4QDAY5\ncrKVl0+dQC1fQsvJdzJ+z7a2KA+++CY+n4/tr75FQ6CHuEPjnboDFDafZtmq1dTU1CQbA5kON2os\ngq/tOMurF/Nu00Gsc1bin7+UcN1bhLBjKHYsyyLHivJu12lK8r0s29R3Amp3uvA5FYKGgWKzJUfL\n9AQDlBaRcfV2TkGMnoa9xNsDmK4ictwRisrLATh14j0ihkXUgFhngJ7ieei6jqZpqDYbge5e1JhO\ncUU5ZscpYnoEu9M1rZrDlJeXc8GSc3i3vSH1vXAqLKupQfPmoweasg6Mfr+fG66+nPq2Xqxui5Cn\nEJvNRjyqc+pEc3IlubmxifxQG6HQh6ZlOJqKhtOoajqZDp+1M7VjsRCzxawIom+++SaKovDYY4/x\n2GOPDbr/i1/8IkuXLgXglltuIScnhwceeIAdO3Ywf/587r77brZu3ZryNdkeJ4QYewO7oWYaWTIZ\nAoEA3/jPX3No7zN87msXDtqa+se7XyBe4OZ0eyOn524gp3wx8+aWkBvpwDINXnn8v/H0vstLL99P\nfrHG3M4Q0Xobx+NRYtF42oAXi8bp6dWI5M3hY//6Q3LOvRjL7kHtjdOtFWLmltFVW0tNTQ3nbdhE\nPKrT1X6ahsOHsbz5nO7pJZpTir7vVZwuN1a4FzNvLorDgZaThzMewrQsFCV1PFVihqhZUZ0cLdPT\ndorCosz1tnPK8/i7Kzfx/Bu7eewUOIvKiEd1GpuaIK8YFAV7PIrHBmZM5+Txd5lbWozdsqCrjYrK\nKuxOF1YsgsP1/ja86dLNU9M0lpcVkbe0EtOIE9P7fo7EHNCRrKSFQiEULYc15XN5re4IMbeXppMn\nk6+nApg5fvyrz5OT9DE0HQLbSE31z9qZ2LFYiNlkVgTRl19+eVjH33TTTdx0001jdpwQYnxomjYl\nw8a2517g0IGdfOzvVqfdmnrdlzfz5H213HDLOu798Us0xbqJs4Qj7x5m4ZxC8mL1fPxLqwcF2F/8\n8FXuvbOWz9w6uGvug7/cxeJLP8vzzz1LT9V6vPklydmjuVo+TQ2HKatcwMG9uzlvwyZieoQ9dQcw\n8orZULMB4lFqa2txrlhPy1t/Zc7aS3BpOViWidHRwoXL1vD20UbUXH/yMQA0b35ylbW9rZVuV5gD\ne/aSZ2TeUtrVFqGqqor58+fz0rfupMvXVwOaCE2WaeBoPoy7uBSKiikt8uMJdbBy8QJer3f0hVDD\nwOdUkuEtId1c0akosZKmnrMiZS7rSFfSPB4PtngEl6axfsVitr+8E9NVADEdxTJx21SK8lzk+4tQ\nbaVykj6GpnpgG62p+FkrI2aEmP5mRRAVQoiJEg6HOXQygDc3knFrqltzEIsafOafLuaX/74Xv3IO\nttXrUA48wcf/Pn2AvfmrG/jhd97iRz/YQ+UcldI5Hk43ddLcprD5b7/AiePHiPjKUOxOTNNMBlGH\n00l51RJ6mo6ihtvpOnaQQ/v2kHtONSsWV505SdOoqamh7p03OWFatLe2Mtev43PZqV6zoq/e1XWK\nLlUlGLWIR3Xszr4uoJo3n9XrLqak/QjRWBzfNf+LnX/8YcbV2zxHUXLsw11f+t986ce/5ES3Bf5y\n1FgEl2JSsKgaq6sde28zanERPdEY8XAv6CEsT15fLWVNTcpjx6M60c62tCUSiW62Y3ViOtrmKGO9\nkpaoV2wyDOx2O3n+QvK9hcnfBcWy8BFIBnc5SR97UzGwzVTpZugONF0uSgkxW0kQFUKIMRQKheiJ\nRCkoynyC5CvKpaczTEm5j7klCkFDRY1HyfNGMwbYBee4eNX+ARo9edgP7EEtu5j5c2wUzq1gz/5D\nWE4PivV+CH3/a53k+QtZ4yvhM5es4rdGjJyqlSnHaN58Vqy5gA72YTo0NlQvSjmBq66az2t1RzDt\nrmRdJry/gnfN1Vv42ZMv4HK5WHX5p3nov+7i4383eGX3md/W8cNv/EfytqqqKu7+x7/ji794kG6P\nB1xF2FUFn92g+sI1hINtvLzjGdp1k2h7C52tzeTaVDZetjW5khju7uTg3t106AYeK8odD29PdtGF\nvlXqN46cwHC42X6w6awddjMJBALpw+MIHm+sV9ISq6x60QJM1Y5DVVFV9f0mSP2C+1Q/SZcuqCKT\nxA6ATKQxlxBTmwRRIYQYQx6PB7ti0tjUnfG4YFsP3vy+k+vSObmc7omRFw9TWJL5hLukxI1jTx1W\nYTlK1RrUrlYuWrmWWCQCnjyUQBcumzooiAKYqh17PIzP50Nx56R9fIfbjd2ME3cPfh7amS2fe198\nFrNFI9TlTVnBsywruULhm1PByiu/wsP33kduTpiCYjcdrRE6AxZ3/Nv3qKpalPLY5eXlrF26ELVs\nMZFIBMuy0DSNeCTEnv0H8V74IbTONi5avhCA2gP17Kk7wLrcvk7CO2trMcuXQ0+A1SsW49I0mgyD\n2+/bhmkY5Favwz7Pgx1wzZkz4mYm49UcZaxW0hKrrH986nleP7Efo7BiUBOkhKl6kj6WQV9MvvG6\noNB/B8BQI2ama8diIWYLCaJCCDGGNE2jp/00LUFnxq2pkXAMLbdvRTHQFsZhdbHqonW89+LzGR+/\nrbmL3Pxz0IMt2HPzqJhbQkFBATE9ghLXceldFPoGz6YEUOI6Vf4cCgoKhlxJSHTCbY/rOByOQfe7\nnE7+Zt0qbrj6cjo6OrAsC7/fj6ZphMNhbPEIsViMWCxGTmEpF3/q60R6ugl3ByldWwBt71FdXZ32\ndSu2m/z5wCE6Yxamakc14/QcO4hneQ12VcXvcSbnbm5YtYy6Iw72vfAMps1OvGghBfFuqlcsTp7s\nqjYbB+M5qF3tnD9GzUymQ3MUv9/PF2/4OGBx3FmCy5M7qI52qp6kSxfUmWMiLijM1I7FQswWEkSF\nEGIMhcNh/HMrcHVG+d3PdvKpW84ftDX10V+9xqUfPTf5/5F2g49+qJrTLo2ubnfGAGsYPq698oOg\n2DhUt4dT7+7hxd52FCNGqPld5i67gPiJ/Vjzz0Xpf2IWj+Nrree6L9x61pWEJdUrOfDqn1GVVSm3\nJ07utmzdwP2Pbx90grnlgnNpqj/I0dNRLLsL1Yz31ZhWzadg7jxMw6B8iPATCARoDHTRc7oHdd4K\nbDYbZixKUM0h0NpBudOges37AVbTNC44dyWdrijxWIz8ZSux21Nfs1gsRmfMQo331Y4ONNxmJkM1\nR0kEb4fDMaXqLq+7amtfMyTvipTbB56kT6UtsNMh6Iuzm6gLCjO5Y7EQs4EEUSGEGEOhUAhnfhGX\nbF3K68/G+fdvvsI58zTmzHER6dHRIzEu/ei5FJflE4vGue/O17nkoo9wojPEm288Qaz0Qh742dNc\nf8vaQQF22z17WXXFl/DNqaA3GCAUDPCBj/xPnA47DpdGTA+zc+dOYm4v/lMH6bXsmA43SjREXk8L\nd9362eSJWaaVBFdHI3d96X+z4809g8Pm1g385tlXBp1g1vf08MAP72H1ug04Dh3BrKhGsdnosixe\nqzvCumULcbUeHXKFYttzL5BbvY5183uSc05jcQMrGsZt6Hhd7rQhKW53oSj2QSEU+gKiqdrB4Sam\np18BHk6d5MDmKOFwmP0Nxwjq8eQKbk5nM83NzVRVVZ318cZCphB5tpN0gHsefGTKbIEdThdUMbVN\n5AWFmd6xWIiZTIKoEEKMoeQIDW8+l3zkYzwcMjhYsoi6tmZyml6nsszDrjdP0dbyLs1tCt3xcvb1\n2OkmjlUwl96mdwkqS/jF7W9SOc9N8ZwcIp1xOtoMFq2+EqcRRT++n5b6g6z70NW4nI6++lAsNG8+\n69at48DuXcR7e1i7dClWqIuq4jKuu/JTKeEim5WEqqqqQSd39zz4SNoTzIPHTsCKTbz33tHkOJdg\n1EoG4Zad9dz5tS+nDTj9A4jmzU/OOQ13d1K7ax+2cyoJdbYmVx37c1smqFba98LhcKCacdTErNHe\nwQFmOHWS/ZujhMNhXtt3GMVXgs2tkHg1Ql1t/OpPL/DP1wY98z0AACAASURBVBeMa5jLdttjupN0\ny7JoamriV0+/iKtqzZTZAjucLqhi6pqssSrSsViI6UeCqBBCjKGUERpOF1UVc9kXjhEqrCRUvoJW\nPYStK0hueTHdrlM4GmoJ5Fdgd3uwRyMUl1dhGQbWsTLOW+jnkx+5guLiYvLz85NhQlEUvv3bHg7v\neTsZ9pINaVat5vyNm+mt38WXP7yRwsLCIVcHsllJ6H9yl2lralCPY3PbCUYtHC53MkzG9L4QaJys\nH/IkMV0AsTtdeAtL8Gt2goaBqdqJx+MpQdQ0DBYV9TVdSrfN2OFwkO9QUO0MqpFMfP1w6iT7v7f7\nG46h+EpSxsRYhkGBy4azas24biEdybbHRA3v7x57mmOdEXbtP0h30UIKDhymump+Sl3tZG2BlS6o\nM4OMVRFCZGtwW0UhhBCjcu3WzZjv1WEaBqsuWId26jD2aC9KdwCiYXJycnDGerG1vouydD3H/vIo\n7+15g8bGRhoP7aOtoQ6zfBm1R09RXl6enLepaRp+v5/29nbeOHycYGEVSsVSbKWVKBVLCRZWUVtb\nS7i7E0XLQ9O0rEJW4nHPduxQJ5jJLbCA2W8brN3pQvPmY3c6M65kZQogy1atRm3cjzKgeVKyznHr\n5pTXuz/TMFhm72Wxz5n2vsTXD8e1WzcTbdhNR1gfFELVxv19z3ect5Bm2vaongmRAyXCa5O3Etvc\nKkJaIfaCOXS5fLxWdyTluU7WFthE0B/4XiVM1QZLIpVcUBBCZEuCqBBCjIFwOEx7e3tfs6Iz217L\nu49jtrxLfn4+Rb0tFIdOstgdo9IWpqTrPfTAabreO0wgZw5tgQBdDXVE3fmEShfT0nCQVt2io6Nj\n0Pd65rW3UcoXpzQjAlBsNsyKag7u3T0uJ3pDnWAmtsAC72+DHSDT8xkqgMSjOliwZtUqFnYdxWg6\nTKjpKPrx/ZR3H+fWj1+FpmlYlsXNV2+hvPs4+vH9Kcf8643X8m83fZzy7uPETxxCP3U85etHMvfz\nM1dcgrftKFbjIYyW41iNh/C1N1DTbzzKeG0hTaxKp2syBUOHyP7hNRaJYDr6wpyiKCj5xexvOJZy\n/GRtgc10UWEkFw7ExJMLCkKIbMnWXCGEGIVMtXqf/8Q11NfXE9RN/AuX47CpdHcG2b1nL/tPtNC7\nYgu4PRDTwekmjkX84MvkLTkfKpYT2P0cHR0dFBQUpGyPbQobFGguuiwrZVUO+sJoh25QrqljfqI3\nVLddxTLJMSL0hEMUOpURjQrp3zxJD73fsMiwu1DaG7nmgmV84vLNuN1uPB4PoVAo7ev+xas2oWna\noG3Gn//ENayprSUSiVBTUzOq16a8vJw11cuwza1Kbj0e+DOP14rPSLY9DtxS7XD3beVOUBSFoB5P\nqcGdrBUr6YI6M2QzViXRaEvXdVwu1yQ+WyHEZJEgKoQQI5SpVu/2+7ZRlufmZMSi7t0T2CIOPIpJ\ntx7nvcOHia/cDHocS7GhAIYRx+ZwEl22kd66v+CcM59wzOKeF/aQ88qeZLi1LAvD7qa6ai6v1R2B\n/OLULaKWhREOcfmGC8blZx4qMOrYaNn1DLlVCwh3dyZXBrOZ5xcOh5Ormg8+/TyP1B7AqFyJzWVR\n4LJTvXY1QaeTXzy+g1s/fhWhUGjI1/2XT/yZWz9+Vdqg6XK5cLlcow7oyUBusyd/zv7Gc8VnJNse\nB4bXxKzYoGEkV9X71+BO9oqVdEGd/jJdUNiydUPK7YHmE5RpCvPmzZMLDULMMhJEhRBihIaq1dOj\nUXZ2OygIBDl/42aKT3cRzPVzrKmRUCxOyOYG1Y6qGhiWiWKBZXNixuOoNhs6NqzcEvz+YrSyBbg0\nLdmI5uart/R15dU01q9YPGh8iM9lZ1HVXMrLy8flZ06cYN73yJMpgbHYZafm3E9wtLGZV59/mjUr\nlpPrcmRcyUq3mtxU38DaS/4HrlwvDocjdSxLv/rHyZ41mc2Kz3g42wzYdCEyXXhdtmo1tbW1yTE7\nia3VPd3d2E8e4drr/2Zcnv9wSBfU6S3dBYV0F5G6bB5OTWK3ZiHE5JEgKoQQI5BpRMH+hmOo/rl0\nNnUTj+osW7Wa1197nQhezKhOWHVgxWJYpokSC2PZnSiYmFgQCUFeMTlGiFJfSXKrZCJkPfnSzmQQ\n0TSNtSuXE4vFkqtZqqJQ3n18XFeQ/H4/ObleNl75UUzLSgmMfr8ffckiitvrufmT1wx6HonteJFI\nhF88viPlhDQWi3H0dJQTe3ZTU1ODfUAIUW026tu6ABs5EzwaYqDJ3EI63BCcLrxq3vzkmJ3W7jDd\ngVZeaTqM16GwtLKCbc+9MGnzRMXM0v+Cwu8ee/qsjbYmuluzEGLySBAVQogRGKpWr7szyMnTrbhL\n3ChnOshq3nxWr6ymefuzdIfCKHHAiGGLRUDzYqKCEUMBVBWc0V4qzjmPIicpK4KJkPXFqzbxyyf+\nnAwiDocjuaUyXRBJhD+PxzMm4ez9EJ4+7LrcblrjKpb1/nzPgaufde+8iXJONSui0eRzisViWHZX\nsuHSeRs2DXps3VSwbDZyMjy/iRoNMVlbSEcSgtOFV82bz5LqlbQ/+ySXfuAy8v1FyVrXyZwnKmam\nyZovKoSYuiSICiHECAzc7hg43cJLL71IQIcuy4Z6ohmts5lz5xWjefPJKyrBblPIXbMZc+d2dEVF\ncXkwQt1Y7lwUmwM1HsFps5FHDJcZo7pq8aDva9jdaJqWVRDJ1EhpYLgYTlgdbsOcgbW08ahOT+4J\n0Ap5re4I61csRtO0ZPddxWYjGLWIR3XsztQmJi7VAtJ340yY6EY7k7GFNNsQ3P99Tfc701J/kPVX\n/k9ycnNTvm4y54mKmUnmiwohBpIgKoQQI9B/u2OwvY0nnt2BuWgtKCpWbxhDtRHpLmX78zu46goH\neYUlWA4X2JwYJQuw7XqO6KIasDtRutrANDBsDhzH3mRJzXouPBPOBkqELLfbnTGIZGqk1H+lazhh\nNWG4DXMG1tImxofYFAXOjA5Zu3I5DocDn8tOl2Ul55H2D6KmYbCoKC/5c2RbIzmTDRWCM72vmqYR\nDodRFIU7HgbXgBCaICtUYizJfFEhxEAyR1QIIUYoMfPwpRf+irloLYpqQ1EUbFgo3W14/KXEl1/C\nyzueIxaJUFA2j54Th1GiEdQVl+BsPYat9Tiq3oOts4XcQy+xptjNxo0b0574pwtZmqbh9/sHBa+h\nGin1r8VKhNUmbyWuymo85QtxVVbT5K3kzoeeIhAIpP25hzMnMN3cy/7jQ/qPDgGorpqP1dmKEg2l\nzCPtP0dSZk1mdrb3NTHr1jTNrFeohBgtmS8qhBhIgqgQQoyQ3+/nf2/dQKgriNITxOrthK52/FaI\nXE8OqsOJYrPTYTkx4lFsRgxboBl7+WJUl4Z98fm4yxaS482naNl55NV8CCWngGjD7lGFrHThr7/E\nStcfn3rurGF1KNmGwXTb8RLjQyzDwIxFiUYiREK9QN/J6rplC5lvdqC/t5/A4d301u+ivPt4chU3\nUSNZ3n0c/fh+Qk1H0Y/vTzlmNsvmIgTICpWYeHIRSQjRn2zNFUKIUTBNE3/FfBzzKjCNOKrNjmqz\nEY/HaQ0EiRgmhuqg9+g+SntPcjyvAJu/EMuy+rrmenP75oBaFm5TJ6T5+H/+Rw2v7Ds84m6s2dRi\n6dhpaAuSXzKyxiHZNswZKuxULljAke1PEfaVYVoKtXoPfreNJdUrMRsPsWbZEk6GLSybjXQ1oTJr\nMr3hNoQZ7igYIUYj3edG/Mwc0S/fdP2sv4gkZoexbiA4nUkQFUKIUSgsLMSm9/StNvU7mbfb7cwt\nKcI0TaJtCt/47HW0trby4e//GiwLRVFQEsdbFkpXK0Xl5RjH2ygoKBhVyMpmpcsKd6N48jMec7bG\nIdmEwXRhJ9zdyZ79Bym48DKM062YsQhmQQntcZ3df/kTiyvnEZyznJx+3XGH6uI6sEYyHA4ntxT7\n/f5Z90d+uA1hJmseqpi9Bn5uNDQ04HQ6JYSKGW8kPRlmOgmiQggxCn6/n4U5CsficVR7mo9U02RJ\nnp28vDzi8TiLfG4aewPoJlg2O4oRx21TKCovx+ZwkqfGkn+QRtqNNZuVrip/Lk3hsek+e7bnOTDs\nHNy7G6uiGruqUpqnceHSaux2Ow6Hgz16J8fthRSn2VaaqYtrIBDgvkeeZMeuQ3TgwrI5KFBjXLay\nihs/esWs+SM/3O22kzkPVcxuic+NEydOTPZTEWLcZdtAcLaRICqEEKN02999is/88B7MFZtSwqgZ\nj2Ps3sGSNYv5zv2P9p3kW3HMUBdz5y3A5nAkt/JaloXZcYrLVlaNyVbIs610XXcmfEzEtsz+Yae+\nrYvWQAc2VwCfy051v+7A8ahOZxzMmEUsFsPhcKQ8zlDbhQOBAN/79YO83dKDtfhinGd+nl7L4pGm\nUzTft41/vfHaUf8c08FIttvKNmchhBhfmWr3Z/OoLGlWJIQQo1RVVcW9X/0885tqie9/Bb1hN/H9\nrzD33VdYPb+c6KJ1ye6l5235MCW2GG1Nx4l3tmOEujA6W/GG21mXG+XGj14xJs8pm4Y+E9k4JBF2\nvvzRy1i7fDGbVy9n7crlKYEyMdbFVO3E4/G0j5Oui+u2516gvjOGNW/F+9ud6evIqxbM4WA8J2Pj\npZlmpO/rUB2YhRBCjFy2DQRnY4dyWREVQogxUFVVxX0/+haBQICOjg4KCwt56Jm/0OStTPnjo3nz\n2bjxYg7s3kW04ziLly7FDSwq9HDt1rHdQnq2la7J2Jbp9/vJtffV0A6UHOtiegathiYM3C4cCATY\ne/wkHbqBbcAfedM0MQyDjohJfVsva3Qdl8s18CFnHNluK4QQU8dwa/dnEwmiQggxhhLjRTJ1L9W8\n+Zy/cTO99bv48t9sorCwMOtVqGAwSGtrK8XFxfh8vuTtmbrwZarhnOhtmZm2jtqdLvLtYDqUtEG1\n/7bSRNOHg6c62HnsNK0RE7f6HkWlpaDakh2LLUXF6unC3dHJpkof8+bNG7efbSqR7bZCCDE1yKis\noUkQFUKIcZDNFVBFy0PTtKwCQkNDPT+++/tECJLrd9ITiOJWCvjcjV/kzSPHR92Fb6SNkUbiqo01\n3PXQUzgWrsbV72c3DYPFPieK2os5IKj27+Lav+mDJ2ceziCop1qIeAo4ceIEpt2NzetDsYMCYHeg\n4+LRnfv4VF7ehPyMU8VEvq9CCCEGk1FZQ5MgKoQQ42Asr4A2NNTztW9/mQ/euByHsyR5eywa56vf\nvZXqD/8j/srq5O0T3YUv25lo/VvX63Y3B55/HMvhomrRYnLt9AXomz4OkHFb6T0PPpJs+qDabPhz\nPQQwCVkmPQ4variLHO+Z1WLLwqWYFGpObJXL2fHGbjZt2jTur8lUIfPqhBBi8smorPQkiAohxDgY\n7hXQTIHh9n//9pkQmvqR7XDa+fgtF/Lg/3cfF13/r8lOsw6HY0K68A1nJtrA1vUuoHDJaqLhENGj\nu/k/1/0Nc+fOTR4/1LbSdFueq6vm0xYIEjmxH6NgPiYKlmmgKCoET1FidrKs5hI6e8M0h61ZUYcj\n8+rGjoR5IcRoSe1+ehJEhRBinFy7dTN3PPAIRslCXJ4c7M6+RjkDt5lmCgzBYJCuWBsOZ1na7+Fw\n2nE4uvlL7dsonjxUM943FqVqftpRJ2NluDPRhmpd79Q82JfV8MSLrw8Kzem2labb8qxpGpsuXMMb\nb+2i5+A7GIaJFWxEUwwWzi1m1QWXoHnz6ewNYzhSG0LMxJAh8+rGhoT5VDPx34oQE0lq9weTICqE\nEOMgcRKrWyoH3n6Dnp4evA6FpZUVLC8rSm7DOVtgaGpqIq8o8x+qojk5dGBD8xYA0GVZvFZ3hNUF\n9nELosOZiZapcVPia7INzUNtedY0jfXrLkB3ejDbGll3/nnk+ouwO50px9lifduhxzpknDx5ksbG\nRioqKlJWdieDzKsbPQnz75NALsTYktr990kQFUKIMdb/JDbft5CLlkIsFiOq69iaDydP4PrXOvbX\nPzBcduFq2k9nrjU93RJGXfZ+ramiKJBfTEN9LR7P2AeObIJlfVsXTU1N+P3+rBo36ZZKU1MT5eXl\nGf9AZ9ry7HA4KHDbUPNy8c0ZvIJsGgZlmkIoFBqzkPH222/zjR/chsNr4C/1EGgJEeux8a2vfpvz\nzz8/q8cYS2MZ+mczCfN9JJALIcaTBFEhhBhjiZNYwzSJ6HqybtPhcGBWrWbbcy9ww9WXZxUYfD4f\n3cG+xkQDa0Sh7/amFovCtfmpd5gmSkzHsqwx//kyBctwdycH9+6mNdBB5LEXybVDuWYj2tmJp3zh\nkMe3t7WCouLijbOutmRq+rDM3ovlc6Z03Y1HdfRQL8a7u9my9eIxCxlvv/02X/v+P/CxL1yQ8t7E\nonG+9v1/5Af/9u8THkZlXt3oSZh/nwRyIcR4kiAqhBBjKBwOc7AlSH2jTlCPY6r2lLpNTdM41hkh\nEAhkFRgA/selH+Hhex7hY58/d1Dg+c1PXidSeXnKiaJlGKiN+1m4ZOm4nCwPtT023N3JztparIpq\nbK4A+ZVLsNvtnDYM6t98hOVli8jx+Qcdb5YvpyCvmLzKJcDZV1v8fj+f/tBG/vD4dgKWE3ue//3t\ngjdeC/SdQB9obuPQ8Ua6Yxa5ubkUO+1sf+0tws5cfNWDQzEML2R844e3DQqh0Fe3+7EvrOUbP7yN\nJx58MvOLOcZkXt3oSZjvI4FcCDHeJIgKIcQYampqYmdDM47Fa7G5FRKncIm6zfUrFmOdOcnNNjD8\n/fWf4GRXiIfvfR1vboSCIjeB1gjvnYyjFtewtMBFb+MhTIcbNRbB51RYVlODGmgal8Ax1PbYg3t3\nY1VUg6ric9mx2/v+xKg2G8s3Xc6BvzzJ+Vd+LPk1B/fuxixfDj0BqlcsTj5OptWWxDxVXekk1++k\nu13HFs/lH275KtXVK5LHXbt1Mz964FFWXvJBXG43drudU6dO0RwK0bDnbS6etxTNO2AV+YxsQsbJ\nkydx5BppV6nhTBOpXIOTJ09OaM2ozKsbPQnzfSSQCyHGmwRRIYQYQ8+89hZ2LaevTrOfRN3mnn37\nma90YbOtzzowuN1uvnXzTWx7biGHTgbo0aMsKHHi9b1H6dpLyfH5iUd1YnoEh0vD7uzbmlpujF/g\nGLg9Nh7VCUYtUFWsztaUYAmQk5vL4iVLKGk/QlPYRLdU2ttaydPyWXxOKQ6bmnJ8utWWhoZ6vv7d\nW9lyw9JB81S//eOv8b2v30lV1SKgb0XUVbVmcJdelwvKF3Fw727O25B+nmg2IaOxsRF/aeZjCko8\nNDc3T3jzIplXNzoS5vtIIBdCjDcJokIIMUbC4TBNYZMCl42gYaD0O4nV208SfecRcvJ14mU5/Nvt\nL6HGctDKV+M/7wNnDQzp2r6HQiHufOgpTG8+dqcr7XiY8TJwJlqvHiMa7qVID1K9YnHaFRJHro/r\nrtqMpmnU1dVRt28fvZ1B3g5FUGOH+lZyV61OrlQOXG358U9vPxNCB2+F3fL/LOXHP72dX9x9b8Yt\nhTabjQLNRUePQTyqJ1+zhGxDRkVFBYGWUMZjOk6HqKioyHjMeJB5daMnYV4CuRBi/EkQFUKIMRAO\nh2lsbETHzrJVq6mtrcWsqEax2dDbT2LffT83/Z/zBtV4Pnffn5mb56HH5csqMPRv++52uyc1cPQP\nxx0dHdz16J/JqVo+5PGJ1ZNQKMS21/bQW7oIe8Gc5PbloGFQW1tLTU0Nmjc/ZbUlGAyiE8ThLE77\n2A6nHZ0gwWAQwzAybimsrprPy+8dRQ/1pgTR4YSMuXPnEuuxZWwiFeuxUVpaetbHGg8yr250JMz3\nkUAuhBhPEkSFEGIU+s/Y0y2V2j11FCw0WLXqXI7XHyIYtYjueYqb/vG8tCt5W29cztuPvclPfvCz\nEQWGqRA4EuF4UWFOVqsnv3vsaVxVayg4cJguy0puY1ZsNsyKag7u3c3qdRenrLa0traS63cOetz+\ncv1O2traKC8vz7ilUNM0aqrmUhlrpen4qRGHjG999dt87fv/yMe+sHbQBYaH//MtfvBv/57V44wn\nmVc3ctn82wqHw4RCITwez4x8nSWQCyHGkwRRIYQYoYEz9lxAYfNpOuxedr3XwvrzLsSI9NLQ9mzG\npjY6QXRdH/VJnWVZ4zKuJVvZrJ703zZbXTWf1+qOQH5xShjt0A2iDe9w7fXvNyoqLi6mJxDN+P17\nAlGKi4uz2lK4rNQ36gB//vnn84N/+3e+8cPbcOQaFJR46DjdN0d0Mka3iPGRLsz3vwCVEs4yjB2a\nrqbCxS4hxMwkQVQIIUYo3Yy9xLZco3w5+xuOsdCfg78k80pJjs9BW1sbPp9v2M9hKp0QZ7N60t7e\nntw2q2ka61csZn/DsZRRNzlWlM9eeWnK8/f5fLjwZdwK68KH0+mkvb2dqzbW8Msn/nzWLYWjXTE8\n//zzeeLBJzl58iTNzc1UVFRM2nZcMTEGXoBKONvYoeluolbXZ/oqsxDifRJEhRBiBIZqiKN586mp\nqeHg3t20t7USty3k1IkAUDXkY7U0d1FcnL72MZOpeEJ8ttWTgZ04NU1j7crlxGIx4vE4DocDo8mW\nDK39T0Y/d+MX+X9/8E9c8/fnp6m1PcCaCz7Md+5/NBmAix0WnDpAa1zFsLuJN5+gTFP48k3Xj/nr\nMnfu3AnvjismR7oLUJB57JA4u6l0UU0IMTEkiAohxAhkmrGnefM5b8Mmuo4d5NMbV/KlFx/MuJIX\nCIDTmbn+MZ2pfEI81OrJUNtmHQ4HDoeD3p4eWuoPcsfDpJyMbrngXP7vzn0svvwrPHzv7/HmhpPz\nVDtbdKrO3YxetQ5Xv8cMnln9vOXqD+B2u2loaMDpdMpJrRixTB2ZIf3YIXF2nZ2dPPzq7il1UU0I\nMf7Usx8ihBBioGxm7LkUE5/Px+KLrmbbPXuJReMp98eicbbds5dF6z5COBwe1vdPnBCnq4GE1BPi\nqebarZsx36vDNIyU23t7etj5zOOUrr0UV2U1nvKFuCqrafJW8pWf/paofx7+8kou/tTXWfLBr5K7\n+EaWXf41cs/9MCfz5qcN5Oo5K3jypZ34/f4RhX0h+st0ASohMXZIZG/HG7uHvKimnrmoJoSYeWRF\nVAghRiDbGXt+v5+8HA8LL7mJh361jTyvTkGRm462CN09Giuu+BKa3jXsofDDOSGeaiszQ9WSttQf\nZN2Wy8nxpa58GKZJsHgRh/fv47wNmwBw53px53qJR3U642DGLGKxGA6HI+Vrp3IgF9NPNheg+o8d\nEmen6zrNYYuKLC6qTbXPMiHE6EgQFUKIETpbl9gtWzdw/+PbqWs4TldOKbbKTUTMCPayeSxdew6u\nnFxMw6C8OzrsLpTT/YR4YC2pruv86L97cHlyBh0bi8Ww7C6CvRbxqJ4y+zMWiWA63JiqPVljOpCs\nUImxku0FKOkqm71IJILhmJ4X1YQQoyNBVAghRihTl9gtWzfwm2dfwVa5klWb5vLKrjriuQV0250c\nPl5PTWn5qIbCT+cT4nA4TCAQSP7/Uy/XcvBUBztPdOA8/To+p8KyVavRvPlAX/2oasYxHW5ieiQl\niDrcbtRYBExP2hAKUzuQi+knmzFFIntutxtbbPpeVBNCjJwEUSGEGIWhusTe8+Aj2CpXood6OLh3\nN1YoSuvp00QjIZyWwdvPPsa1WzaOaij8dDshDgQC3Pfo0zy/r4EO04ER6qar/TTnnH8xq5Yuwd7r\nRMkvJmgY1NbWUlNTg+bNx+Fw4HPZ6ewK4XClrojYnS7y7WA6FOz2wX/SpnIgF9NTNmOKRPZcLhdl\nmoJ5lotqlmUN6qQthJjeJIgKIcQY6N8lNtFIyLT1sLO2FquiGkeJjTIgrkeIhXvoOfo2V22sGdVJ\n63Q6IQ4EAnz/vm3UdjtQF9bgUhRa6t4kvGorhwMBevYewqs5iVgWis2GWVHNwb27kzWhy+bP48C7\nuwedqJqGwWKfE0XtHXQiO1UDuZj+zjamSAzPlgtX89z+urQX1XoP1NJb4E0ZzSRjXYSYGSSICiHE\nGEs0Ejq0dzdWRTWKzUast5vAsUPolorpcGNZOfzjHT/nP2/7p1GH0elwQrztuRc4FM9B9ReiKApm\nLIpuqSg2O+QX094bwOu2sDpbIb8YxWYjGO2rCVVtdlytR7nr1s+y4809g0P3TR9Pfo+pHsjFzDLU\nmCIxPPn5+WkvqhXbYvSqKsE5y1NGM8lYFyFmBgmiQggxxjweD1a4i2DUSobQU0fqiFcsR1FtWJaF\nmePnuMvgjgce45+v/5tRn0xN5RPicDhMfVsvnTGweRQAzGhfkyEAFAXdhJ44rFs2nyPvNRHU48T1\nCL31u1lWVpQMlFVVVUOG7ukQyIUQ6aW7qPa7x54mZ+7UnJUshBg9CaJCCDHGNE1jrlvBsLuwA4Fj\nh4hXLMdCobc3RMy0sMd1miIx3nbkcN+jT3Prp6+f7KedtXA4TCgUyrpWKxQKEVFUTFUlcTqpOvua\nDJln/t+y2YlbCg6Hg7UrlxOLxQgdjXHbp67B5/OlPF6m0D2VA7kQ4uwS/4YTJQ4un4x1EWKmkiAq\nhBDj4BNXXMYj3/0Fhn8uuqViodDVGwabHdUIk5PnQ+0NEs4t5pE3X+cTlzdTVlY22U87o0AgkH77\n61lqtTweD27LRDXN5G2qw4lLMQmZBopqQzHi2BV7svOtTVVZVuobFEKFELPDdJ6VLITIjjrZT0AI\nIWaisrIyrrlgGZ6ukximRU9XJxhx7HEdr9eLqqq4bQo2ux2rsIIHn94x2U85o0AgwE8efJImbyWu\nymo85QtxVVbT5K3kzoeeShnHMpCmaSwqyiHfoWBZVvJ2//yl2BsPYBlxXCr4PU7sdvv7TYa2bp6I\nH00IMQVN91nJQoizkyAqhBDj5MaPXsF5fjvluS5yejd3zgAAIABJREFUnA7y8/PIzctDVVWUrlaK\nSksBsMV1TkYswuHwJD/joW177gVslelrtdQztVqZXLt1M8vsvZgdp5Jh1JHjpbRqGZ4DL+IPHGW+\nZqEf309593FpQiLELJeYlWwaRtr7ZTSTENPfrA2iX/nKV1i2bFna+/7whz9wxRVXsHr1aj7ykY/w\npz/9aVTHCSFmJ7/fzz9ffw0V8Tas3gCEuqCrHXeog/LycuxOF5Zh4HMqKJp3ygbRRK1Wuhl/kFqr\nNRS/38+/3ngt15Q7yDlai37kbaIHd1LQtIfPfXAd2771Jb720c188apN3HD15RJCxaiFw2Ha29un\n7L8rcXbXbt2M+V7doDAquyaEmBlmZY3os88+y/bt21EUZdB99957L3fccQdXXnklN910E8899xz/\n8A//gKIoXHHFFcM+Tggxu/n9fn7yL7fwiW/eDZXzsTlcyUBnGQZq436W1dSgBpqm7BazsarV8vv9\n3Prp6/n7cJhAIICqqhQUFBAKhUZUeypEOiOtZRZTz3SalSyEGL5ZF0SDwSDf+ta3cDqdxGKxlPu6\nu7v52c9+xtVXX82PfvQjAD72sY9xww03cMcdd3D55ZejKErWxwkhBEB5eTl/e9FK/nrsBJ1xMBx9\nHWN9ToVlNTW4PLmUG1N3i9lY12ppmkZ5eTnwfu2prXJlSndMmRMoRkJ+n2ae6TIrWQgxfLNua+63\nv/1t/H4/W7duHXTfjh07iEQiXHfddcnbFEXhk5/8JCdPnuTtt98e1nFCCJFw4zUf5tzyAi5edyEb\nzl3GJRvWc96GTbg8uVN+i9l41mqNtvZ0OpAtohNnNvw+zVaapuH3+yWECjGDzKog+vzzz/PMM8/w\nve99D6fTOej+uro6AKqrq1Nur66uxrIs9u3bN6zjhBAiIbHFrFI/hRpoItraSG/9Lnwn93Hz1Vum\n/CrNeNRqjUXt6UhMVDAMBALc8+AjfOf+R/nRYy/ynfsf5Z4HH8nYYViM3GT9PgkhhBiZWbM1t7Oz\nk29+85vceOONnHvuufzxj38cdExLSwt5eXm4XK6U24uLiwE4efLksI4TQoj+ElvMmpqaeOiZv3JS\nsXFKyeFnT74wqhq2cDhMKBTC4/GM2zy98ajVmug5gZ2dnex4YzexnfvHvXZQtohOPJk7KYQQ08u0\nDqJtbW0Z7/d4PMmapW9/+9vk5OTwla98Zcjje3t70/5xSmwDCYVCwzpOCCEGCgQC/OLxHdgqV5Jj\nG11AmeimLGNdqzWRcwIDgQC//0styrxqys7Up8L4BcNMW0Q5s0X085+4Zsy+n5C5k0IIMd1M6yC6\ncePGjPfffPPNfPnLX2bHjh08/fTT/O53v0u7Jbe/TE2G+t+X7XHDtXv37hF/rZga4vE4IO/lTDAe\n7+X/ff5FWguqUFtbB91nOgu5+zcP8D8v23TWx+ns7EwGK9V15sTa5qG91+Ct//g113+ghvz8/DF7\n3uPF0d1Gc1NT2u2UpmEwp7uNQ4cOjfr7/N/nX8QqX4aiKJw6dSr1+wzjdc+Gruu8ceQE9nlDB57m\nEydYU1s7aGeNyM5Q/zYn6vdJjB35mzlzyHsphmtaB9Hvfve7Ge+vrq6mq6uLb37zm3z4wx+mqqqK\njo4OLMtC13UAOjo6cLlcydXTSGTw1dTEbbm5uQBZHyeEEP3puk5z2MJeNHQNW3O47/PpbAFlxxu7\n+0JomhU3c141O97YPWbBajxtuXA1D/ylFnPAz2IaBpzYz5YP1Iz6eyRed9U/+tc9G5FIBMPhzvgH\n1nC4iUQiEkTH2ET8PgkhhBgb0zqIXnvttWc9pra2lra2Np544gkef/zxQfevX7+ea665httvv525\nc+fS2dlJLBbD4XAkjzl9+jQApaWlAFkfNxKrV68e8deKqSFxJVDey+lvrN/L9vZ2/GVNeObMGfKY\nkBFi0aJFGbeJhsNhYjv3p2wxHUiPd7JkyZJpUQu3cuXK9FuM/8+nx2S7bOJ171L7+vPNSfP6Z/O6\nZyscDrP9YBOuDO+zrgeoqamRDqAjlOnf5nj/PomxJX8zZw55L2eOt956a0K+z7QOotlYvnw5v/71\nrwfd/qtf/YpXX32V3/zmN8kmQ4mutwcOHODcc89NHrt//34URWHVqlXDOk4IIfobqxq2mdaUZbzn\nBCZfd9vQr+tY1g4mxt00GcaQW0RHOu5GnJ3MnRRCiOlhxo9v8Xq9rF+/ftB/JSUlAFx00UVUVVUB\ncOmll+J0Ovn973+f/HrLsvjDH/5AWVkZa9asGdZxQggxULEtRjScvqFZtgFFURRi3R3EYrEhj5mO\nTVlGOydwqLEs4zkHdSjjMe5GDI/MnRRCiKltxq+IDofP5+Nzn/scP//5zzEMg4suuohnnnmGd955\nh7vuuivZhCjb44QQAlK72/bEHex69k8UFBazfPUaNG9fQ6FkQPn4VVk9zsF3j9PdbaNAc1FdNT9l\n5XO2rbhl0z342q2bees/fo05L3X+czav+0iMx7gbIYQQYiaZ1UE0XWC85ZZbyMnJ4YEHHmDHjh3M\nnz+fu+++m61bt47oOCHE7DZwnqQL2FC2gLojDbz6/NOsWbGcXJfjrAFl4OMs95ZQ+8YbdMyr5rW6\nI6xfsRhN08YtWE1V2c7r9Pv9XP+BGna8sRs93jkhwVC2iAohhBBDUyzLsib7SQjYu3cv0WiUtWvX\nTvZTEaMkxfozx1i8l/c8+AhN3sq0tYJ6JEJxez03f/JvzxpQEo+jR6PsbzhGUI8TjUToPNGAZXNQ\nkeemZtmCcZ0jOhVlen1Nw6C8+3hyXmfi/VyyZIkEw2lOPmdnDnkvZw55L2eOt956C6fTOe59b2b1\niqgQQoyncDjMsc5Iykpdfy63m9a4ylDXA8PhMKFQCEVRONYZwXRFeW3fYRRfCTa3gpYPWuk8jKhO\naN9LfG7rdSxYsGA8f6Qp5Wyvr2qzcawzMqhpk6Zp06KJkxBCCDGTSRAVQohxMtLutgNrHmPdHRx8\n9zhmSy9KQcWgsgKb04VVtohH//wyt35mcoJoIjR7PJ4JC3kzrXuwEGJ0JuNzSAgxchJEhRBinIxk\nXEu6msdYLEZnUOXkkQOUrfThyPGmeRydkxFtwkNXNo2CxstYjcMRQkxvk/k5JIQYuRk/vkUIISbL\nSMaGbHvuBWyVK1NqHh0OB16njVjFcgLHDg16HMsw8DkVFM07aHTJeEqE5mPuMozi+ThK5uGqrKbJ\nW8mdDz1FIBAY1+8/GWNZhBBTS+JzqMlbiauyGk/5wgn9HBJCjJwEUSGEGEcD50nGYjFCoRB6JDJo\nnmSi5jFd450Vi+Zj6w4QMcGMRZO3W4aB2rifZatWT/jq332PPs3uHhsv1x3hlUPHeXHPQd7adwA9\nGkU9ZwXbnnth3J+DzOsUYnZLd/EO+mrEJ+pzSAgxMrI1VwghxlFinuR9jz7N8/sa6DAdKEaMAnS2\nrFmacmymmkdN06gqzuPEsXaM43VYXj9qLILPqbCspgaXJ5dyY+JW/5qbm3nkzQOo1RuxeRQSp4Bd\nlpUcJ5OuUdBYk3mdQsxeI21YJoSYGiSICiHEOOvs7KS+sZnlay/C7nLjcGnYnU6CA2Zdpqt5DIfD\n749rUd3EuoPkFeWzeGEZeYWl2J3OSZkd+uDTz2MVzhvUOMkyTQy3l72HG1hZ5JmQE0CZ1ynE7CQN\ny4SY3iSICiHEOGloqOfHd3+f9vAp8kpzOPXii3T3aKz44I345lT0bSU7s3Xs85+4Jlnz2GQYqDYb\n4XA4ZVyL2zCoXlyFWlDKO6+/wpoVy8l1OSZ89S8cDnMyYmGL68nb4lGdtpYWIoaFZbNzItyF2RRE\nv+KiCXlOIGNZhJhtpGGZENObBFEhhBgHDQ31fP27t7LlhqU4nCXJ22PRONvu+SnVV3wpGUb7bx27\ndutm7nzoKThnBfsbjqH4SlAUJVkLuqqmBs2bj75kEcXt9dz8yWsmfPUvFAqhaHn4nF0EDQPDiNPY\n1AR5xaAoKIBps4MR5z8fez654puJjF0Q05n8/k6OgRfvBpKGZUJMbRJEhRBiHPz4p7efCaGpH7MO\np51rP7+Kh++9j4s/9XUgdetYoubxj089T8fR/SiFFSm1oJo3HwCX201rXMWyrAn/2RKrEMtWraa2\ntpZT5CZDKIBlGthP1LHiyg+h5vmSK77pyNgFMZ3J7+/k63/xrn8YnYySBSHE8EgQFUKIMRYMBtEJ\n4nAWp73f4bTjzQ0T6enGnesdtHXM7/fzv67cwtGQhaNwbrKmdKDJqn1KrkJ4cjl/zRpOPfkEav4c\nTIcbNRbBhck5ixfjLSgEGLJZSLqZqQBNA2pnhZiK5Pd3apCGZUJMXxJEhRBijLW2tpLrHxwc+yso\nchPuDuLUPGm3jnk8HlyKievMCmg6k1n7lFiFUAsqKFq0AsU/FzOmo9idKKFO1qxYnDx2qMCcaexC\n/9pZIaYi+f2dOqRhmRDTk8wRFUKIMVZcXExPIJrxmI62CO6cvCFnXSZWHfVIhFAoRCwWS7l/smuf\nEqsQldHTWO2NGJFelFgEnxli/YrFKaEzXWDONDMVUscuCDHVyO/v1JQob5AQKsT0ICuiQggxxnw+\nHy58xKLxQTWi0NewqLPNYKHZPuTWsUAgQG9PNy//5VGMc1Ziw8LnslNdNR+X0zklap/8fj9fvOET\nwEMc95Tjcrux21N/3qECs4xdENOZ/P4KIcToyYqoEEKMg3/60r+y4/eHiEXjKbfHonGeu+8AP/3O\nHXz+E9cMGUJ/8uCTBOeuZMNlV1CsB1C7A3QEO3n12afwnTowperPrrvqMlytR1EHzBRNNgtJs+Ir\nYxfEdCa/v0IIMXqyIiqEEOOgqmoR3/v6nfz4p7ejEyTX76QnEMWFj9tvu5uqqkVDfm3/2jPNm895\nGzYRj+rE9Ag2+2py9JNTJoTCyJqFjNfYBRmjISaCjA0RQojRkyAqhBDjpKpqEb+4+16CwSBtbW0U\nFxeTnz908yF4v/asfxdOALvThd3pAuDY6fRdaCfTSJqFjOXYBRmjISaajA0RQojRkSAqhBDjzOfz\n4fP5sjp2uteeaZqW9fMaq7ELMkZDTAYZGyKEEKMjQVQIIaaQ2VZ7NhZjF2SMhpgsMjZECCFGToKo\nEEJMIbO19iybldR09Z9DbWVOkDEaYiIMZyeAEEKIPhJEhRBikgzVWEdqz1Jlqv+0LCvrrcxCCCGE\nmDokiAohxAQ7W2MdqT1739nqP2++esus2soshBBCzBQSRIUQYgJl21hHas/6nK3+88mXds7KrcxC\nCCHEdKeO9gHi8dRh7bt27eLee+/lT3/6E7FYbLQPL4QQM0qmYKWeaazTn6Zp+P3+jEEqHA7T3t4+\n47afJuo/0wVMeL/+86qNNZjv1WEaRsr9ya3MWzdPxNMVQgghxDCMeEXUNE1uv/12tm3bxssvv0xO\nTg4PP/wwt912W/KY5cuX87vf/Y7c3NwxebJCCDGdDaexTjaNT2b67MxsR9lomiZbmYUQQohpZsRB\n9Le//S33338/CxYsIBQK4XK5+MlPfoLb7eZf/uVfaGxs5Ne//jW/+MUv+Od//uexfM5CCDEthUIh\ndEvF7OrE4XZjd7oGHZPtjNDZMDtzOKNs3G63bGUWQgghppERB9HHH3+cpUuXsm3bNhwOB6+++iod\nHR1cd911XHfddQA0NDTw7LPPShAVQsx6gUCA//7TDmr31KEUdqLGIvicCstWrUbz5iePy7axzmyY\nnTmSUTYyRkMIIYSYHkZcI3rs2DE2btyIw+EA4IUXXkBRFC699NLkMYsXL6alpWXUT1IIIaazxOrl\n6cJFFCysRi05B6ViKcHCKmprawl3dwLZN9bJtnZyqtaMDqem9dqtm6X+UwghhJiBRrwiqmkauq4n\n//+FF17A4XBQU1OTvK21tXXabw0TQojR6r96WV01n9fqjkB+MYrNhllRzcG9u1m97uKsZ4RmWzuZ\nba3pRBlJTauMshFCCCFmphEH0cWLF/P888/z6U9/ml27dnHs2DE2b96cPOnZu3cv27dv5+KLLx6z\nJyuEENPNwAZFdrudNQsrqD9xku64ianaaW9rpaT9CNdlGayGUzs5VYymplVG2QghhBAzz4iD6Gc/\n+1m+8IUvsGXLFgBUVeUzn/kMAD/96U/5r//6LxwOBzfffPPYPFMhhJiGEquX4XCY/Q3HCOpxTNWO\napp47QqL5pVi99u47qotWa/ujaR2crKNRU2r1H8KIYQQM8eIa0Q3bdrEb3/7W7Zu3cpll13GL3/5\ny+S2XL/fzyWXXMLvf/97Vq5cOWZPVgghpptoNErbscO8/OYuulw+bPnFOLwF2PKL6fUUsutoI1ak\nZ9irl9OpdnK617QKIYQQYuyNeEWU/5+9O42Psrz3P/6dTLaZkICDbEFJFBUIS0ApglKDS6yAWKgB\nCkoVWxULFsVWqQtSxHIqLYuAbSmoFYiKeEAFlCXn/Kmt7YFCtCWAC8oipiwZSGJyT0gm9/+Bzugw\nISSTyWz5vF+vPuC+rplc9Ac437k2Sf3791f//v39nt9+++26/fbbm/LWABDV9u//RL9d+GtVWUql\n5Bq1Pl6tY3sSlNB3lJLadpIkWSwWma0cKvl8e6NnL6Np72S07mkFAADNp0lBFADgb//+T/TY7Ad1\n/YRuSkhs731efbpGK5asUFX2BCW17STT7Zb1yF45Ol0QUAiLlr2T0binFQAANK8GB9FBgwYF9AMs\nFovee++9gF4LANHot8/O+TqE+v4Tm5AYrwmT++mPv3tZiX2Gf3WP6IABMstKmjQbGOl7J6NxTysA\nAGheDQ6ikfwhBwAixalTp1SlU0pIbFdne0JivLqkJ6hrdi+1Oq+tJKnKeSTmZwPzcnM0f/UGqUtP\nnzDq3dPagGtrAABA7GhwEP2f//mf5hwHAMSE48ePq5Ujsd4+jvY2VbsqJbVtMbOB0bSnFQAAND/2\niAJAELVr105fOk/X2+fkCZfaX3Fei5sNjJY9rQAAoPk1KYhWVFTo73//u0pKSuR2u2Wapreturpa\np06d0t/+9je99tprTR4oAESDNm3aKEltVH26xm+PqPTVgUWnSkzpxCF1DsJsoGEYqqyslN1uj5ot\nFJG+pxUAADS/gIPop59+qh/96EcqKSnxeW6apiwWi/fXiYn1L1EDgFjz85/9Uo89/aCuv933wKLq\n0zXaumKf5j46S1lZWU2aDXQ6nXUvc83NYZkrAACIeAEH0WeffVYnTpzQ8OHDdeWVV2rRokXq2bOn\nrr32Wn3yySdas2aNUlJStGHDhmCOFwAiXteul+jpx+brt8/OUZVOqZUjUV86TytJbfTrxxeoa9dL\nmvT+TqdT815dL2tGLyW1+ebgnyNut+av3qAH2XMJAAAiXMBBdMeOHerXr59+97vfSZJ27typw4cP\na+zYsZKk4cOH6/bbb9dLL72k+++/PzijBYAo0bXrJfr9wuU6deqUTpw4oXbt2ql169ZBee81W7bJ\nmtHL7yqUOKtV6tJTa7Zs0z1jRwXlZwEAADSHuEBfWFpaqn79+nl/3a1bN+3du9e7T7Rv377KyclR\nQUFB00cJAFGqTZs2uuSSS4IWQg3D0IFSV533cUpfhdEDpS4ZhhGUnwcAANAcAg6irVq1Uk1NjffX\nXbp0kcvl0sGDB73Punbtqi+++KJpIwSAFsowDJWUlPiEysrKSrnj699b6o5PJogCAICIFvDS3J49\ne+ovf/mLfv7znysxMVFdu3aVaZp6//33lZmZKUk6fPiwz8FFAIBzq+8gIrvdLmuNq97XW2tcstvt\nIRotAABA4wU8Izpu3DgdOHBAt956qwoLC3XRRRepW7dueuaZZ/Taa6/pueee05YtW9SrV69gjhcA\nIlZdM5iN5TmI6EhqhpIysmTvfLGSMrJ0JDVD81dvkGEYymydrFq3u87X17rdymydzP2cAAAgogU8\nI3rDDTdo+vTpWrRokY4ePSpJevTRR3XvvfdqxowZMk1TaWlpmjZtWtAGCwCRKJhXqTTkIKK83BzN\nX71B6tLTp1+t263aQ0XKGzM8KL+vaBGNd6kCANDSBRxEJenOO+/U+PHj5f76m/krr7xSGzdu1Nat\nW5WUlKQhQ4aoQ4cOQRkoAESiYF6l4jmI6Nvv822eg4hsNpseHDO87vDbgq5u4S5VAACiV5OCqCQl\nJib6/Do9PV0/+tGPmvq2ABAVgnmVSmMOInI4HLpn7CgZhiHDMGS321vUclzuUgUAILoFHETXrVvX\n4L4jR44M9McAQMRq6AymZ8/ouZaPBnIQkc1ma5HLUblLFQCA6BZwEJ0+ffo5T8Q1TVMWi4UgCiAm\nNWQG88uqav3h5f/W8Zq4cy4ftdlsymydrCNud533hDbXQUTRtseyMV8ARMPvBwCAlijgIPrEE0/U\n+dwwDB06dEgbN25Ujx49NHXq1IAHBwCR7FwzmEZ5qd7fvVepw0cpKSnJ+7y+5aOhPIgoWvdYNmYJ\nM0EUiEyeL8Cqqqp8/n0E0HIEHERvu+22etvvuusu3Xrrrdq7d6/69+8f6I8BgIh1rhnMvR+8r9aX\n9FLiGR+y6ls+6nA4QnIQUTTvseQuVSB6nfkFmPOLw0q3WXThhRdG7L85AJpHkw8rOpvMzEwNHTpU\nK1eu1IQJE5rrxwBAWJ1tBvO0UamTJcd11RUD6nxdfctHQ3EQUTTvsQzXEmYATVPXF2BlVrv+EwVf\ngAEIvrjmfPOUlBQVFxc3549olLffflu33nqrsrOzdf311+t3v/udqqqq/Prl5+dr6NChys7O1ogR\nI7Rx48Y636+h/QDELs8MZufyg6o6uEeVRz5V1cE9Or/kY/XtlVXv0lDP8tGzsdlscjgczbIn9ECp\nq84QJ/kfshSJ8nJzVHuoSLVfXx/m4V3CnJsTppEBOJv6vgCL+/oLMAAtR7PNiH722Wdav369Onfu\n3Fw/olFWr16tGTNm6Oqrr9ajjz6qoqIiLVu2TP/5z380d+5cb7/ly5dr7ty5GjZsmCZOnKgtW7Zo\n2rRpslgsGjp0aKP7AYh9dc1gmqapp1bUf7p4uJaPxsIey1AtYQYQHBwyBuBMAQfRvLy8Op/X1taq\noqJCn3/+udxut+65556ABxcsp06d0jPPPKPvfve7Wrp0qfe0X7vdrj//+c/62c9+pgsvvFDl5eVa\nvHixbrnlFj3zzDOSpNGjR2vChAmaO3eubrrpJlkslgb3A9CynHmVSiQtH/32ybixsseypd+lCkST\nWPgCDEBwBRxEd+/efda2hIQEXXLJJRozZsw5DzUKhc2bN6uiosI7Y+kxbtw4paSkqLa2VpJUUFAg\nl8ulcePGeftYLBaNHz9eDz30kHbt2qUrrriiwf0AtGyhPAH3bM52Mm67+FqdipCQ3FQt9S5VIJrE\nyhdgAIIn4CC6b9++YI6jWe3atUutW7dWjx49JElVVVWKj49XRkaG7r//fm+/oqIiSVJWVpbP67Oy\nsmSapnbv3q0rrriiwf0AtGzhWD767ZlPwzDOejJuxefbZSn9p+zd+octJANoOThkDMCZmm2PaCQ5\ncOCAOnXqpMLCQj399NPavXu3EhISNGzYMD3xxBNq1aqVJOno0aNKS0vzu8+qXbt2kuQ9eKmh/QDg\nbPtHKysrg7oEra6ZzyOf7FOHK4YopY6DQVJ6DFCb4t1KKT/IHksAIREJq0QARI4GB9F16+o/dKM+\nI0eODPi19Tlx4kS97Z69UOXl5SovL9ePf/xjjRkzRpMmTVJhYaFefPFFFRcX66WXXpIkVVRU1Pmh\n0PPtXGVlZaP6AYCHzWaTYRh66Y23/YNfbk6Tgl9dVyJUV1fr02OndXjnTg0YMEC21NY+r4mzWnXc\nnaBJt9wkSeyxBNDs6lolUvP1PaJTJ97GF2BAC9PgIDp9+nSf/ZWmafr92uPMg3qaK4gOHjy43vb7\n7rtPU6dO1enTp3X8+HFNnTpVkyZNkiTdcMMNSklJ0aJFi7Rt2zbl5OTUOfZv+3ZbQ/s11gcffBDw\naxEZampqJFHLWBDMWpaWlmrl/26X5cIsxSV9vQfKaldJhVs7Fz2v264doNatW9f/Jmfx31v/ouPn\ndVXc8ePeZ1VVVaqocivuvC7a/rd31a1vf7/XVZ0s144dOwL+udGGv5uxg1pGtyu7X6y+VVVyuVxK\n6NFRiYmJOnz4sA4fPhzuoaEJ+HuJxmpwEH3iiSd8fu12u7V06VJ9+eWXGjlypPr166c2bdqooqJC\n//73v/X666/rvPPO089//vOgD9pj9uzZ9bZ79nB6Zi/PPOl35MiRevbZZ7V9+3bl5OTIbrfL5fLf\nSO955lnC29B+AOBRsOODr0JoHctkay/MUsGOD/SDG65p9PtWVVXpC8NU/Pm+7xsfHy9LbY0scVaV\nu+NUc/q04hMTffpYq10c8gMgLJKSkpSUlOQNLwBangYH0TNPv33uuedUUVGhVatWqWfPnj5tw4YN\nU15ensaOHavCwkLl5uYGZ7RnONsVMmfq0KGDPv74Y78lH23btpX01VJbSerUqZNKS0tVXV2thIQE\nb79jx45536cx/QKRnZ0d8GsRGTzfBFLL6BesWhqGoer/26P0eu5Vrqop1WWXXdboYFhSUiJH+hHZ\nO3b0a/vPyTKVJaWotk1btT2vtc/y3Fq3W52TK/Wd73ynUT8vmvF3M3ZQy9hBLWMHtYwdO3fuDMnP\niQv0ha+++qpuvPFGvxDq0bVrV91000168803Ax5csHjG+Mknn/g8//zzzyVJ6enpkr459Xbv3r0+\n/fbs2SOLxaLevXs3qh8ASI27P6+x6rsSIatrpszS47KcrlRC0jcB13swSG5Oo38eAABAMAQcRMvK\nyhr0zX0gH6yCbfjw4bJYLPrTn/7k8/zPf/6z4uLidP3110uShgwZosTERK1cudLbxzRN5efnKz09\nXX379m1UPwCQmvf+PM+VCLVud51tV3a/WJnZhDLxAAAgAElEQVS1J+Uu/kSVRz5V1cE96lx+UA9y\nMi4AAAijgK9vueyyy7R161b99Kc/Vfv27f3aDx48qE2bNkXE7OCll16qiRMn6vnnn1dlZaWuueYa\nbd++XRs3btSECRPUtWtXSVKbNm109913a8mSJXK73Ro4cKA2bdqkwsJCLViwwHsIUUP7AYDU/Pfn\n1XclQtLxTzV/+lTvqb2cjAsAACKBdebMmTMDeWHbtm21Zs0abdq0SZJUW1ursrIyHThwQOvXr9eT\nTz6pL7/8Ur/+9a+9S1/D6eqrr1bbtm317rvvasOGDaqsrNSkSZN0//33+/QbMGCAUlJStGXLFm3a\ntEmJiYmaMWOGbrzxxoD6NdSxY8fkdrsj4v8rNM3Ro0clSR3r2LOH6BLMWl6U3kF/f+9vUtr5ssR9\nsxjFs0z2x7fkBnxwkM1mU9+LL9Tnez9QydFina4oV+3JYnWxVOjHt+TK4XAoISFBNptN8fEt4vro\nOvF3M3ZQy9hBLWMHtYwdxcXFslqtTTr3piEs5rfvXWmkNWvW6De/+Y3Ky8v9rnJxOBx66qmnvMte\nUb9///vfOn36tK644opwDwVNxGb92BHsWjqdTp/784J1j+i3GYbBzOdZ8HczdlDL2EEtYwe1jB07\nd+5UYmJis69sbdJX43l5efre976n//f//p8+/PBDlZWVKS0tTT179vRehwIA+IrD4dA9Y0c1a1i0\n2WxcyQIAACJek9dopaamasSIERoxYkQwxgMAMY+wCAAAWroGB9FVq1apT58+3inaVatWNfiHnHkH\nKQAAAACg5WpwEH3qqac0ZcoUbxB96qmnZLFYdK4tphaLhSAKAAAAAPBqcBCdM2eOevTo4fNrAAAA\nAAAaq8FBdNSoUfX+GgDQcIZhqLKyUna7nf2iAACgxWnyYUU1NTU+99K9//772rlzp9LT03XDDTco\nISGhqT8CAGJGKK5wAQAAiHQBB9Ha2lrNmTNHa9as0V//+lelpKTotdde04wZM7x9evTooZdeekmt\nWrUKymABIJo5nU7Ne3W9rBm9lNTG6n1+xO3W/NUb9OCY4YRRAADQIsQF+sIXX3xRK1asUMeOHVVZ\nWamamhrNmzdPycnJmjFjhu666y7t3btXv//974M5XgCIWmu2bJM1o5firFaf53FWq+K69NSaLdvC\nNDIAAIDQCnhG9M0331S3bt20Zs0aJSQk6L333tPJkyc1btw4jRs3TpK0f/9+bd68Wb/4xS+CNmAA\niEaGYehAqctnJvTb4qxWHSh1yTAM9owCAICYF/CM6IEDBzR48GDvHtBt27bJYrFoyJAh3j6XXnqp\njh492uRBAkC0q6yslDs+ud4+7vhkGYYRohEBAACET8BB1Gazqaqqyvvrbdu2KSEhQQMGDPA+O378\nOPudAECS3W6XtcZVbx9rjUt2uz1EIwIAAAifgIPopZdeqq1bt+qLL77Qxo0bdeDAAQ0aNMi7pOzf\n//633nnnHfXs2TNogwWAaGWz2ZTZOlm1bned7bVutzJbJys5uf5ZU+BsDMNQSUkJs+oAgKgQ8B7R\nn/zkJ/rpT3+q66+/XpIUFxenH//4x5KkZ599Vn/84x+VkJCg++67LzgjBYAol5ebo/mrN0hdevoc\nWFTrdqv2UJHyxgwP4+gQrbgSCAAQjQIOotdcc41efPFFvfTSSzJNU6NHj/Yuy3U4HPrud7+rKVOm\nqFevXkEbLABEM4fDoQfHDK87NHB1CwLAlUDhZxiGKisrZbfbOWgMABoh4CAqSf3791f//v39nt9+\n++26/fbbm/LWABCTHA6H7hk7SoZhyDAM2e12luMiYPVdCaSvrwS6Z+yoMI0utjETDQBN06Qg6rFr\n1y7t3btXZWVluu+++/TZZ58pLS1Nbdu2DcbbA0DMsdlszJ6gSbgSKHyYiQaApgv4sCJJ+uCDD3TT\nTTfptttu01NPPaVnn31WkvTWW29pyJAhWrVqVVAGCQAAfHElUPjUNxMd9/VMNACgfgEH0Y8//lh3\n3nmnjh8/rgkTJujaa6/1tnXv3l2pqamaPXu2/vKXvwRloAAA4BtcCRQenpnoM0Oox7dnogEAZxdw\nEH322WcVFxen119/XY8++qjPNS033nij1qxZo7S0NC1fvjwoAwUAAN/gSqDwYCYaAIIj4CC6fft2\nDRs2TJmZmXW2p6en66abbtJHH30U6I8AgJjEfY8IlrzcHNUeKvILo94rgXJzwjSy2MVMNAAER8CH\nFblcLqWkpNTbJzExkQ9aAPA1TtlEsHElUOh5ZqKPuN11Ls9lJhoAGibgIHrRRRfpH//4h0zTlMVi\n8WuvqanR3/72N1100UVNGiAAxAJO2URz4Uqg0MvLzdH81RukLj19wuhpo1JV+9/XzeO+H8bRtWzc\n6wpEj4CDaF5enmbPnq3HH39c06dP92k7efKknnrqKX322Wd69NFHmzxIAIhWng9Fr2ws4L5HNCuu\nBAqdM2eiv6yq1qcffSgzIUldL7lUi9dvY7VDiLHiBIg+AQfR2267TYWFhXr99de1du1aJSUlSZJy\nc3P1xRdfyO126/rrr9ftt98etMECQLT49oeiKjNO2/9VpPMurlVW10y/sMB9j0D08cxEHzlyRPNX\nb1B27veV+PVnIYnVDqHEihMgOgV8WJHFYtHvfvc7zZs3T4MGDVJycrKsVqtKS0t1+eWX69e//rWW\nLFlS57JdAIhlng9FR1IzlJSRpQRHJ1naXqCypDb6e9HHde6d55RNIDpt+Ot2pXTr7xNCJe4UDSXu\ndQWiU8Azoh7Dhg3TsGHD6mw7fPiwZs6cyRUuAFqUMz8UJSQnK67a9dUXc63bac/+A7qiVw+f13DK\nJhB9PHeKfnsW7ttY7dD8qAEQvRoVRJ1Op1588UX94x//0OnTp9WjRw/de++9fle4uN1uLV++XL//\n/e/lctV/xDkAxJK6PhTFJyapTaJFp9xuWaxWnaqqUXV1tRISEiRxyiYQrRpzpyghqHlQAyB6NTiI\nFhcX64c//KGOHTsm0zQlSfv27dP69ev1xz/+UVdddZUkae/evXr44Yf1ySefyDRNfe9732uekQNA\nBDrbh6LuvbO1fft21V6Qpdq4eNXU1CghIeGb+x7HDA/DaAE0BXeKhh81AKJXg/eILlmyREePHtXV\nV1+tFStWaMOGDZo+fbri4+M1c+ZMSdIbb7yhsWPH6uOPP9YFF1ygpUuXauHChc01dgCIOGf7UGRL\nba0BAwaoTcl+WQ7vUfWxw6o6uEedyw9ykAYQpTx3ita63XW2s9qh+VEDIHo1eEb0//7v/5SRkaE/\n/OEPio//6mVdu3ZVSkqKZsyYoeXLl+u3v/2trFar7rnnHk2ePNl7ki4AtBT1XXZvS22t7CuvVvuS\njzVu+PUt4r5H7vRDrDvbnaKsdggdagBEpwYH0RMnTuj73/++N4R6XHfddXriiSc0b948derUSQsW\nLFCfPn2CPlAAiBbn+lA0rgXMgHKnH1qKM+8U9fnz3gL+rkcCagBEpwYHUcMwdP755/s9P++88yRJ\nKSkpevnll9WhQ4fgjQ4AolBL/1DEnX6Rj5nq4PLcKWoYhgzDaBGrHSINNQCiT5Ovb4mL+2qb6fDh\nwwmhAPC1lvyhqL47/fT1nX73jB0VptG1bMxUN49vB3v+fwwvm83GlytAlGhyEPVo27ZtsN4KAGJG\nS/tQxJ1+kYuZ6uAj2ANA4Bp8ai4AAOfSmDv9GsswDJWUlAT0WtQ/Ux339Uw1Gs4T7I+kZigpI0v2\nzhcrKSNLR1IzNH/1BjmdznAPEQAiWqNmRPft26d169Y1um3kyJGNHxkAIOo0x51+zDo1HTPVwccS\ndABomkYF0YKCAhUUFNTZtnXrVr820zRlsVgIogDQQtR3fY301cnBnW1WVVRUyDTNc4YelpMGR2Nm\nqgmi50awB4Cma3AQnTJlSnOOAwAQI852fU3Fl19q71/e0SWZXfTMG39p0Mwms07B0Rwz1S0ZwR4A\nmo4gCgAIqrqur6n+8pQ+/vBD9bjme0pp803orG9mk1mn4GnITHVm6+QWc7JzUxHsAaDpOKwIABB0\nnutrnpgwUo+MzFFXR4ouHz7GJ4RK9R+U05wHH7VEebk5qj1UpFq32+d5rdut2kNFysvNCdPIoo8n\n2J/5/6UHwR4Azo0gCgBoNp7ra45U1j0TJ/nObH4bs07B5Zmp7lx+UFUH96jyyKeqOrhHncsPstc2\nAAR7AGiaoN0jCgBAXQLdT8dy0uDzzFQbhiHDMGS32/n/L0B1LUH37nsm2APAORFEAQDNqikzm2c7\n+Mg76zRmeNDH2xJ4ZqrRNAR7AAgcQRQA0KyaMrPJrBOiAcEeABqPIAoAaHZNmdlk1gkAgNhDEAUA\nNLtgzGwy6wQAQOwgiAIAQoKZTQAA4EEQBQCEFDObAACAe0QBAAAAACFFEAUAAAAAhBRBFACAKGYY\nhkpKSmQYRriHAgBAg7FHFACAKOR0Ous+hTg3h/tVAQARjxlRAIgyzIDB6XRq3qvrdSQ1Q0kZWbJ3\nvlhJGVk6kpqh+as3yOl0hnuIAADUq8XMiLrdbj333HNau3atjh07pvT0dI0fP1533nmnX9/8/Hyt\nWLFCX3zxhbp06aL77rtPw4YNC7gfAAQDM2DwWLNlm6wZvRRntfo8j7NapS49tWbLNt0zdlSYRgcA\nwLm1mBnRJ598UkuWLFGfPn30+OOPq3v37vqv//ovLViwwKff8uXLNWvWLPXo0UOPPfaYOnbsqGnT\npuntt98OqB8ABAMzYPAwDEMHSl1+IdQjzmrVgVIXM+YAgIjWIoLoiRMn9Prrr+uGG27QggUL9MMf\n/lDPPvuscnJytHz5cpWXl0uSysvLtXjxYt1yyy2aN2+exowZo6VLl6p///6aO3euTNNsVD8ACJb6\nZsDivp4BayiW9ka3yspKueOT6+3jjk+mvgCAiNYigujnn38u0zR11VVX+Ty/5pprVFNTo88++0yS\nVFBQIJfLpXHjxnn7WCwWjR8/XsXFxdq1a1ej+gFAMARrBszpdGrpq2v11Ip1euaNv+ipFeu09NW1\nzKZGGbvdLmuNq94+1hqX7HZ7iEYEAEDjtYggesEFF8hqtXoDp8ehQ4ckSe3atZMkFRUVSZKysrJ8\n+mVlZck0Te3evbtR/QAgGIIxA8bS3thhs9mU2TpZtW53ne21brcyWycrObn+PzMAAIRTiwii559/\nvu677z699tprev3113XkyBGtXbtWr7zyioYPH65OnTpJko4ePaq0tDQlJSX5vN4TVIuLixvVDwCC\nIRgzYMFc2ovwy8vNUe2hIr8wWut2q/ZQkfJyc8I0MgAAGiaqT809ceJEve12u937wezWW2/Ve++9\np8cee8zb3r9/f/3617/2/rqiokI2m83vfTzfKldWVjaqHwAEg2cG7IjbXefy3HPNgHmW9ia1OffS\n3rr+bUPkcTgcenDM8LpPUR4znFOUAQARL6qD6ODBg+ttv++++zR16lQdPXpUo0ePVkVFhaZOnarL\nLrtMRUVFWr58uX7yk59o2bJlSkxMlPTVXs+z+XZbQ/sBQDDk5eZo/uoNUpeePmHUOwM2ZvhZX9uY\npb0E0ejhcDh0z9hRMgxDhmHIbrezHBcAEDWiOojOnj273nbPHs4VK1aopKREv//97zVkyBBJ0vXX\nX6/LLrtMDzzwgF599VVNmDBBdrtdLpf/8jfPs1atWklSg/sF4oMPPgj4tYgMNTU1kqhlLIi0WuZm\nZahgx9/0uWHKnZAsa7VL6TaLcr+TrcOHD+vw4cN1vq6qqkrOLw6rzHr2pbs1XxzW/v37z/oesSDS\n6onAUcvYQS1jB7VEY0V1EM3Ly2tQv48//lgpKSneEOpx0003yWazafv27ZowYYI6deqk0tJSVVdX\nKyEhwdvv2LFjkqQOHTpIUoP7AUAwtW7dWj+44RpVVVXJ5XLJZrN5V3PUJykpSek2i/5Tz9LedJul\nQe8FAAAQDFEdRBvK8+HKNE2fZbOe+z5ra2slfXPq7d69e9WnTx9vvz179shisah3796N6heI7Ozs\ngF+LyOD5JpBaRr9YquWFF16o+as3KO4sS3unTrwt5vcVxlI9WzpqGTuoZeyglrFj586dIfk5LeLU\n3KuvvloVFRV68803fZ6vW7dOhmFo4MCBkqQhQ4YoMTFRK1eu9PYxTVP5+flKT09X3759G9UPACKF\n53CbzuUHVXVwjyqPfKqqg3vUufygHuRwGwAAEGItYkb01ltv1bp16/TYY4/pX//6l7p3766ioiK9\n9tprysrK0tixYyVJbdq00d13360lS5bI7XZr4MCB2rRpkwoLC7VgwQLvbGpD+wFAJOFwm5bDMAxV\nVlbKbrdzABUAICK1iCCakJCgF154QYsWLdI777yjV199Ve3atdPtt9+u+++/32df1JQpU5SSkqJV\nq1apoKBAmZmZWrhwoXJzc33es6H9AKApmiNQ2Gw2wkmUaGz9nU5n3Ve65OYw6w0AiCgtIohKX33w\nevjhh/Xwww+fs+/EiRM1ceLEoPUDgMYiULRsgdTf6XRq3qvrZc3o5XNn7BG3W/NXb2AJNgAgorSI\nPaIAEE08geJIaoaSMrJk73yxkjKydCQ1Q/NXb5DT6Qz3ENGMAq3/mi3bZM3o5XcycpzVqrguPbVm\ny7ZQDB8AgAYhiAJAhCFQtGyB1N8wDB0oddV5PY/ntQdKXTIMo1nGDABAYxFEASCCEChatkDrX1lZ\nKXd8/QdPueOT+XMDAIgYBFEAiCAEipYt0Prb7XZZa1z1vs5a45Ldbm/yGAEACAaCKABEEAJFyxZo\n/W02mzJbJ6vW7a7zNbVutzJbJ3NdDwAgYhBEASCCEChatqbUPy83R7WHivxeW+t2q/ZQkfJyc5pl\nzAAABIIgCgARhkDRsgVaf4fDoQfHDFfn8oOqOrhHlUc+VdXBPepcfpCrWwAAEafF3CMKANHCEyjq\nvEeSQBHzmlJ/h8Ohe8aOkmEYMgxDdrud2XMAQEQiiAJABCJQtGxNrb/NZpPNZmvGEQIA0DQEUQCI\nYASKlo36AwBiFXtEAQAAAAAhRRAFACCGGIahkpIS7poFAEQ0luYCABADnE5n3Qcc5eZwwBUAIOIw\nIwoAQJRzOp2a9+p6HUnNUFJGluydL1ZSRpaOpGZo/uoNcjqd4R4iAAA+CKIAAES5NVu2yZrRS3FW\nq8/zOKtVcV16as2WbWEaGQAAdSOIAgBajKqqqpjbP2kYhg6UuvxCqEec1aoDpa6Y+j2HA3tvASC4\n2CMKAIh5TqdT/731L/rCMOVIPxJT+ycrKyvljq//jlF3fLIMw+AqmACw9xYAmgczogCAmObZP/mf\n87oq/sJuMbd/0m63y1rjqrePtcYlu90eohHFDvbeAkDzIYgCAGJarO+ftNlsymydrFq3u872Wrdb\nma2TlZxc/6wp/MX6nx0ACCeCKAAgZrWU/ZN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NZ+plJAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120122b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the linear model and visualizer \n",
    "print(\"Ridge Scores and Target Visualization Below:\\n\")\n",
    "fit_and_evaluate(dataset, Ridge, \"Facebook  Ridge\", 'NA')\n",
    "\n",
    "ridge = Ridge()\n",
    "visualizer = ResidualsPlot(ridge)\n",
    "visualizer.fit(fit_and_evaluate(dataset, Ridge, \"X_train\",'Ridge_vis')[0], fit_and_evaluate(dataset, Ridge, \"y_train\",'Ridge_vis')[1])  # Fit the training data to the visualizer\n",
    "visualizer.score(fit_and_evaluate(dataset, Ridge, \"X_train\",'Ridge_vis')[2], fit_and_evaluate(dataset, Ridge, \"y_train\",'Ridge_vis')[3])  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pwitt/anaconda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build and Validation of Facebook ElasticNet took 15.888 seconds\n",
      "Validation scores are as follows:\n",
      "\n",
      "Mean Absolute Error:       8.311814\n",
      "Mean Squared Error:      882.415546\n",
      "Median Absolute Error      4.139396\n",
      "R2                         0.301708\n",
      "dtype: float64\n",
      "\n",
      "Fitted model written to:\n",
      "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook-elasticnet.pickle\n"
     ]
    }
   ],
   "source": [
    "fit_and_evaluate(dataset, ElasticNet, \"Facebook ElasticNet\", 'NA')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}