{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Applied Machine Learning: Module 3 (Evaluation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 178\n",
      "1 182\n",
      "2 177\n",
      "3 183\n",
      "4 181\n",
      "5 182\n",
      "6 181\n",
      "7 179\n",
      "8 174\n",
      "9 180\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_digits # Database of images of hand written digits.\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target\n",
    "\n",
    "for class_name, class_count in zip(dataset.target_names, np.bincount(dataset.target)):\n",
    "    print(class_name,class_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original labels:\t [1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n",
      "New binary labels:\t [1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Creating a dataset with imbalanced binary classes:  \n",
    "# Negative class (0) is 'not digit 1' \n",
    "# Positive class (1) is 'digit 1'\n",
    "y_binary_imbalanced = y.copy()\n",
    "y_binary_imbalanced[y_binary_imbalanced != 1] = 0\n",
    "\n",
    "print('Original labels:\\t', y[1:30])\n",
    "print('New binary labels:\\t', y_binary_imbalanced[1:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1615,  182])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_binary_imbalanced)    # Negative class (0) is the most frequent class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90888888888888886"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "# Accuracy of Support Vector Machine classifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='rbf', C=1).fit(X_train, y_train)\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "DummyClassifier is a classifier that makes predictions using simple rules, which can be useful as a baseline for comparison against actual classifiers, especially with imbalanced classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "# Negative class (0) is most frequent\n",
    "dummy_majority = DummyClassifier(strategy = 'most_frequent').fit(X_train, y_train)\n",
    "# Therefore the dummy 'most_frequent' classifier always predicts class 0\n",
    "y_dummy_predictions = dummy_majority.predict(X_test)\n",
    "\n",
    "y_dummy_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9044444444444445"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_majority.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97777777777777775"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binary (two-class) confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent class (dummy classifier)\n",
      " [[407   0]\n",
      " [ 43   0]]\n"
     ]
    }
   ],
   "source": [
    "# sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)\n",
    "# Right column with TP and FP is 0. This makes sense since the classifier has been set to predict the negative class.\n",
    "# You are comparing the correct test data with your classifier's prediction.\n",
    "# Successful predictions on the diagonal of the matrix.\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Negative class (0) is most frequent\n",
    "dummy_majority = DummyClassifier(strategy = 'most_frequent').fit(X_train, y_train)\n",
    "y_majority_predicted = dummy_majority.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, y_majority_predicted)\n",
    "\n",
    "print('Most frequent class (dummy classifier)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random class-proportional prediction (dummy classifier)\n",
      " [[374  33]\n",
      " [ 37   6]]\n"
     ]
    }
   ],
   "source": [
    "# produces random predictions w/ same class proportion as training set\n",
    "dummy_classprop = DummyClassifier(strategy='stratified').fit(X_train, y_train)\n",
    "y_classprop_predicted = dummy_classprop.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, y_classprop_predicted)\n",
    "\n",
    "print('Random class-proportional prediction (dummy classifier)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support vector machine classifier (linear kernel, C=1)\n",
      " [[402   5]\n",
      " [  5  38]]\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
    "svm_predicted = svm.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, svm_predicted)\n",
    "\n",
    "print('Support vector machine classifier (linear kernel, C=1)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression classifier (default settings)\n",
      " [[401   6]\n",
      " [  6  37]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "lr_predicted = lr.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, lr_predicted)\n",
    "\n",
    "print('Logistic regression classifier (default settings)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree classifier (max_depth = 2)\n",
      " [[400   7]\n",
      " [ 17  26]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "tree_predicted = dt.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, tree_predicted)\n",
    "\n",
    "print('Decision tree classifier (max_depth = 2)\\n', confusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation metrics for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95\n",
      "Precision: 0.79\n",
      "Recall: 0.60\n",
      "F1: 0.68\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "# Accuracy = TP + TN / (TP + TN + FP + FN)\n",
    "# Precision = TP / (TP + FP)\n",
    "# Recall = TP / (TP + FN)  Also known as sensitivity, or True Positive Rate\n",
    "# F1 = 2 * Precision * Recall / (Precision + Recall) \n",
    "print('Accuracy: {:.2f}'.format(accuracy_score(y_test, tree_predicted)))\n",
    "print('Precision: {:.2f}'.format(precision_score(y_test, tree_predicted)))\n",
    "print('Recall: {:.2f}'.format(recall_score(y_test, tree_predicted)))\n",
    "print('F1: {:.2f}'.format(f1_score(y_test, tree_predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.96      0.98      0.97       407\n",
      "          1       0.79      0.60      0.68        43\n",
      "\n",
      "avg / total       0.94      0.95      0.94       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combined report with all above metrics\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, tree_predicted, target_names=['not 1', '1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random class-proportional (dummy)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.91      0.92      0.91       407\n",
      "          1       0.15      0.14      0.15        43\n",
      "\n",
      "avg / total       0.84      0.84      0.84       450\n",
      "\n",
      "SVM\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.99      0.99      0.99       407\n",
      "          1       0.88      0.88      0.88        43\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n",
      "Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.99      0.99      0.99       407\n",
      "          1       0.86      0.86      0.86        43\n",
      "\n",
      "avg / total       0.97      0.97      0.97       450\n",
      "\n",
      "Decision tree\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.96      0.98      0.97       407\n",
      "          1       0.79      0.60      0.68        43\n",
      "\n",
      "avg / total       0.94      0.95      0.94       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Random class-proportional (dummy)\\n', \n",
    "      classification_report(y_test, y_classprop_predicted, target_names=['not 1', '1']))\n",
    "print('SVM\\n', \n",
    "      classification_report(y_test, svm_predicted, target_names = ['not 1', '1']))\n",
    "print('Logistic regression\\n', \n",
    "      classification_report(y_test, lr_predicted, target_names = ['not 1', '1']))\n",
    "print('Decision tree\\n', \n",
    "      classification_report(y_test, tree_predicted, target_names = ['not 1', '1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, -23.172292973469546),\n",
       " (0, -13.542576515500063),\n",
       " (0, -21.717588760007867),\n",
       " (0, -18.903065133316439),\n",
       " (0, -19.733169947138638),\n",
       " (0, -9.7463217496747667),\n",
       " (1, 5.2327155658831135),\n",
       " (0, -19.308012306288916),\n",
       " (0, -25.099330209728528),\n",
       " (0, -21.824312362996),\n",
       " (0, -24.14378275072049),\n",
       " (0, -19.578811099762508),\n",
       " (0, -22.568371393280199),\n",
       " (0, -10.822590225240777),\n",
       " (0, -11.907918741521932),\n",
       " (0, -10.977026853802803),\n",
       " (1, 11.206811164226373),\n",
       " (0, -27.64415761980748),\n",
       " (0, -12.857692102545409),\n",
       " (0, -25.848149140240199)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "y_scores_lr = lr.fit(X_train, y_train).decision_function(X_test)\n",
    "y_score_list = list(zip(y_test[0:20], y_scores_lr[0:20]))\n",
    "\n",
    "# show the decision_function scores for first 20 instances\n",
    "y_score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 8.6377579220606777e-11),\n",
       " (0, 1.3138118599563783e-06),\n",
       " (0, 3.6997386039099529e-10),\n",
       " (0, 6.1730972504865465e-09),\n",
       " (0, 2.6914925394345074e-09),\n",
       " (0, 5.8506057771143608e-05),\n",
       " (1, 0.99468934644404694),\n",
       " (0, 4.1175302368500096e-09),\n",
       " (0, 1.2574750894253029e-11),\n",
       " (0, 3.3252290754668869e-10),\n",
       " (0, 3.2695529799373086e-11),\n",
       " (0, 3.1407283576084884e-09),\n",
       " (0, 1.5800864117150149e-10),\n",
       " (0, 1.9943442430612578e-05),\n",
       " (0, 6.7368003023860014e-06),\n",
       " (0, 1.7089540581641637e-05),\n",
       " (1, 0.9999864188091131),\n",
       " (0, 9.8694940340195476e-13),\n",
       " (0, 2.6059983600823893e-06),\n",
       " (0, 5.9469113009063784e-12)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Positive values have a high probability close to 1 whereas negative values have a very low probability.\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "y_proba_lr = lr.fit(X_train, y_train).predict_proba(X_test)\n",
    "y_proba_list = list(zip(y_test[0:20], y_proba_lr[0:20,1]))\n",
    "\n",
    "# show the probability of positive class for first 20 instances\n",
    "y_proba_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# You can use either the decision function or predict_proba to find a range of values over which you can vary the decision threshold for a precision-recall curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision-recall curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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MGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGA0ZW4AADgUSURBVGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYuB06EAAAAAAQ5G+9wAiFkAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMHADAXaRAAGsLEZEAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numpy.argmin(a, axis=None, out=None)[source]\n",
    "# Returns the indices of the minimum values along an axis.\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_scores_lr)\n",
    "closest_zero = np.argmin(np.abs(thresholds)) # Index of the lowest threshold.\n",
    "closest_zero_p = precision[closest_zero]\n",
    "closest_zero_r = recall[closest_zero]\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([0.0, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.plot(precision, recall, label='Precision-Recall Curve')\n",
    "plt.plot(closest_zero_p, closest_zero_r, 'o', markersize = 12, fillstyle = 'none', c='r', mew=3)\n",
    "plt.xlabel('Precision', fontsize=16)\n",
    "plt.ylabel('Recall', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -4.04847662,  -3.93819545,  -3.48628627,  -3.44776445,\n",
       "        -3.33892603,  -2.5783356 ,  -2.37746137,  -2.34718536,\n",
       "        -2.30446832,  -2.15792885,  -2.03386685,  -1.87131487,\n",
       "        -1.7495844 ,  -1.72691524,  -1.68712543,  -1.47668716,\n",
       "        -1.33979401,  -1.3051061 ,  -1.08033549,  -0.57099832,\n",
       "         0.13088342,   0.17583273,   0.47631823,   0.6418365 ,\n",
       "         1.00422797,   1.33670725,   1.68203683,   1.69861005,\n",
       "         1.87908244,   2.18989765,   2.43420944,   2.55168221,\n",
       "         3.71752409,   3.80620565,   4.21070117,   4.25093438,\n",
       "         4.30966876,   4.31558393,   4.55321241,   4.57143325,\n",
       "         4.93002949,   5.23271557,   5.73378353,   6.12856799,\n",
       "         6.55341039,   6.86404167,   6.92400179,   7.22184672,\n",
       "         7.37403798,   7.80959453,   8.26212674,   8.3930213 ,\n",
       "         8.45858117,   9.84572083,   9.87342932,  10.201736  ,\n",
       "        11.20681116,  11.4821926 ,  11.55476419,  11.68009017,\n",
       "        13.26095216,  14.73832302,  16.02811865])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curves, Area-Under-Curve (AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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MGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGA0ZW4AADgUSURBVGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYuB06EAAAAAAQ5G+9wAiFkAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMHADAXaRAAGsLEZEAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC: Receiver Operating Characteristic Curve.\n",
    "# True positive rate vs False positive rate.\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "y_score_lr = lr.fit(X_train, y_train).decision_function(X_test)\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_score_lr)\n",
    "roc_auc_lr = auc(fpr_lr, tpr_lr)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([-0.01, 1.00])\n",
    "plt.ylim([-0.01, 1.01])\n",
    "plt.plot(fpr_lr, tpr_lr, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_lr))\n",
    "plt.xlabel('False Positive Rate', fontsize=16)\n",
    "plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)\n",
    "plt.legend(loc='lower right', fontsize=13)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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MGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGA0ZW4AADgUSURBVGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYuB06EAAAAAAQ5G+9wAiFkAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMHADAXaRAAGsLEZEAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 0.01  accuracy = 0.91   AUC = 1.00\n",
      "gamma = 0.10  accuracy = 0.90   AUC = 0.98\n",
      "gamma = 0.20  accuracy = 0.90   AUC = 0.66\n",
      "gamma = 1.00  accuracy = 0.90   AUC = 0.50\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([-0.01, 1.00])\n",
    "plt.ylim([-0.01, 1.01])\n",
    "for g in [0.01, 0.1, 0.20, 1]:\n",
    "    svm = SVC(gamma=g).fit(X_train, y_train)\n",
    "    y_score_svm = svm.decision_function(X_test)\n",
    "    fpr_svm, tpr_svm, _ = roc_curve(y_test, y_score_svm)\n",
    "    roc_auc_svm = auc(fpr_svm, tpr_svm)\n",
    "    accuracy_svm = svm.score(X_test, y_test)\n",
    "    print(\"gamma = {:.2f}  accuracy = {:.2f}   AUC = {:.2f}\".format(g, accuracy_svm, \n",
    "                                                                    roc_auc_svm))\n",
    "    plt.plot(fpr_svm, tpr_svm, lw=3, alpha=0.7, \n",
    "             label='SVM (gamma = {:0.2f}, area = {:0.2f})'.format(g, roc_auc_svm))\n",
    "\n",
    "plt.xlabel('False Positive Rate', fontsize=16)\n",
    "plt.ylabel('True Positive Rate (Recall)', fontsize=16)\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=0.5, linestyle='--')\n",
    "plt.legend(loc=\"lower right\", fontsize=11)\n",
    "plt.title('ROC curve: (1-of-10 digits classifier)', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation measures for multi-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-class confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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     "output_type": "display_data"
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AQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG/gMDvDsAAX+9zF0AAAAASUVORK5CYII=\" width=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
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AQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCA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width=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target\n",
    "X_train_mc, X_test_mc, y_train_mc, y_test_mc = train_test_split(X, y, random_state=0)\n",
    "\n",
    "\n",
    "svm = SVC(kernel = 'linear').fit(X_train_mc, y_train_mc)\n",
    "svm_predicted_mc = svm.predict(X_test_mc)\n",
    "confusion_mc = confusion_matrix(y_test_mc, svm_predicted_mc)\n",
    "df_cm = pd.DataFrame(confusion_mc, \n",
    "                     index = [i for i in range(0,10)], columns = [i for i in range(0,10)])\n",
    "\n",
    "plt.figure(figsize=(5.5,4))\n",
    "sns.heatmap(df_cm, annot=True)\n",
    "plt.title('SVM Linear Kernel \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test_mc, \n",
    "                                                                       svm_predicted_mc)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "\n",
    "\n",
    "svm = SVC(kernel = 'rbf').fit(X_train_mc, y_train_mc)\n",
    "svm_predicted_mc = svm.predict(X_test_mc)\n",
    "confusion_mc = confusion_matrix(y_test_mc, svm_predicted_mc)\n",
    "df_cm = pd.DataFrame(confusion_mc, index = [i for i in range(0,10)],\n",
    "                  columns = [i for i in range(0,10)])\n",
    "\n",
    "plt.figure(figsize = (5.5,4))\n",
    "sns.heatmap(df_cm, annot=True)\n",
    "plt.title('SVM RBF Kernel \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test_mc, \n",
    "                                                                    svm_predicted_mc)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-class classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.65      0.79        37\n",
      "          1       1.00      0.23      0.38        43\n",
      "          2       1.00      0.39      0.56        44\n",
      "          3       1.00      0.93      0.97        45\n",
      "          4       0.14      1.00      0.25        38\n",
      "          5       1.00      0.33      0.50        48\n",
      "          6       1.00      0.54      0.70        52\n",
      "          7       1.00      0.35      0.52        48\n",
      "          8       1.00      0.02      0.04        48\n",
      "          9       1.00      0.55      0.71        47\n",
      "\n",
      "avg / total       0.93      0.49      0.54       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_mc, svm_predicted_mc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Micro- vs. macro-averaged metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro-averaged precision = 0.49 (treat instances equally)\n",
      "Macro-averaged precision = 0.91 (treat classes equally)\n"
     ]
    }
   ],
   "source": [
    "print('Micro-averaged precision = {:.2f} (treat instances equally)'\n",
    "      .format(precision_score(y_test_mc, svm_predicted_mc, average = 'micro')))\n",
    "print('Macro-averaged precision = {:.2f} (treat classes equally)'\n",
    "      .format(precision_score(y_test_mc, svm_predicted_mc, average = 'macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro-averaged f1 = 0.49 (treat instances equally)\n",
      "Macro-averaged f1 = 0.54 (treat classes equally)\n"
     ]
    }
   ],
   "source": [
    "print('Micro-averaged f1 = {:.2f} (treat instances equally)'\n",
    "      .format(f1_score(y_test_mc, svm_predicted_mc, average = 'micro')))\n",
    "print('Macro-averaged f1 = {:.2f} (treat classes equally)'\n",
    "      .format(f1_score(y_test_mc, svm_predicted_mc, average = 'macro')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear model, coefficients:  [-698.80206267]\n",
      "Mean squared error (dummy): 4965.13\n",
      "Mean squared error (linear model): 4646.74\n",
      "r2_score (dummy): -0.00\n",
      "r2_score (linear model): 0.06\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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     "output_type": "display_data"
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "X = diabetes.data[:, None, 6]\n",
    "y = diabetes.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "lm = LinearRegression().fit(X_train, y_train)\n",
    "lm_dummy_mean = DummyRegressor(strategy = 'mean').fit(X_train, y_train)\n",
    "\n",
    "y_predict = lm.predict(X_test)\n",
    "y_predict_dummy_mean = lm_dummy_mean.predict(X_test)\n",
    "\n",
    "print('Linear model, coefficients: ', lm.coef_)\n",
    "print(\"Mean squared error (dummy): {:.2f}\".format(mean_squared_error(y_test, \n",
    "                                                                     y_predict_dummy_mean)))\n",
    "print(\"Mean squared error (linear model): {:.2f}\".format(mean_squared_error(y_test, y_predict)))\n",
    "print(\"r2_score (dummy): {:.2f}\".format(r2_score(y_test, y_predict_dummy_mean)))\n",
    "print(\"r2_score (linear model): {:.2f}\".format(r2_score(y_test, y_predict)))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_predict, color='green', linewidth=2)\n",
    "plt.plot(X_test, y_predict_dummy_mean, color='red', linestyle = 'dashed', \n",
    "         linewidth=2, label = 'dummy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection using evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-validation example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation (accuracy) [ 0.91944444  0.98611111  0.97214485  0.97493036  0.96935933]\n",
      "Cross-validation (AUC) [ 0.9641871   0.9976571   0.99372205  0.99699002  0.98675611]\n",
      "Cross-validation (recall) [ 0.81081081  0.89189189  0.83333333  0.83333333  0.83333333]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dataset = load_digits()\n",
    "# again, making this a binary problem with 'digit 1' as positive class \n",
    "# and 'not 1' as negative class\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "clf = SVC(kernel='linear', C=1)\n",
    "\n",
    "# accuracy is the default scoring metric\n",
    "print('Cross-validation (accuracy)', cross_val_score(clf, X, y, cv=5))\n",
    "# use AUC as scoring metric\n",
    "print('Cross-validation (AUC)', cross_val_score(clf, X, y, cv=5, scoring = 'roc_auc'))\n",
    "# use recall as scoring metric\n",
    "print('Cross-validation (recall)', cross_val_score(clf, X, y, cv=5, scoring = 'recall'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid search example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid best parameter (max. accuracy):  {'gamma': 0.001}\n",
      "Grid best score (accuracy):  0.996288047513\n",
      "Test set AUC:  0.999828581224\n",
      "Grid best parameter (max. AUC):  {'gamma': 0.001}\n",
      "Grid best score (AUC):  0.99987412783\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "clf = SVC(kernel='rbf')\n",
    "grid_values = {'gamma': [0.001, 0.01, 0.05, 0.1, 1, 10, 100]}\n",
    "\n",
    "# default metric to optimize over grid parameters: accuracy\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values)\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "y_decision_fn_scores_acc = grid_clf_acc.decision_function(X_test) \n",
    "\n",
    "print('Grid best parameter (max. accuracy): ', grid_clf_acc.best_params_)\n",
    "print('Grid best score (accuracy): ', grid_clf_acc.best_score_)\n",
    "\n",
    "# alternative metric to optimize over grid parameters: AUC\n",
    "grid_clf_auc = GridSearchCV(clf, param_grid = grid_values, scoring = 'roc_auc')\n",
    "grid_clf_auc.fit(X_train, y_train)\n",
    "y_decision_fn_scores_auc = grid_clf_auc.decision_function(X_test) \n",
    "\n",
    "print('Test set AUC: ', roc_auc_score(y_test, y_decision_fn_scores_auc))\n",
    "print('Grid best parameter (max. AUC): ', grid_clf_auc.best_params_)\n",
    "print('Grid best score (AUC): ', grid_clf_auc.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation metrics supported for model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "\n",
    "print(sorted(list(SCORERS.keys())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-feature classification example using the digits dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizing a classifier using different evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid best parameter (max. precision): {'class_weight': {1: 2}}\n",
      "Grid best score (precision): 0.5304958279022508\n",
      "Grid best parameter (max. recall): {'class_weight': {1: 50}}\n",
      "Grid best score (recall): 0.9427531767926396\n",
      "Grid best parameter (max. f1): {'class_weight': {1: 3}}\n",
      "Grid best score (f1): 0.5103116662855149\n",
      "Grid best parameter (max. roc_auc): {'class_weight': {1: 5}}\n",
      "Grid best score (roc_auc): 0.8912720538697216\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from adspy_shared_utilities import plot_class_regions_for_classifier_subplot\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# Create a two-feature input vector matching the example plot above\n",
    "# We jitter the points (add a small amount of random noise) in case there are areas\n",
    "# in feature space where many instances have the same features.\n",
    "jitter_delta = 0.25\n",
    "X_twovar_train = X_train[:,[20,59]]+ np.random.rand(X_train.shape[0], 2) - jitter_delta\n",
    "X_twovar_test  = X_test[:,[20,59]] + np.random.rand(X_test.shape[0], 2) - jitter_delta\n",
    "\n",
    "clf = SVC(kernel = 'linear').fit(X_twovar_train, y_train)\n",
    "\n",
    "# 'balanced' is a 1:1 ratio between the positive and negative class!\n",
    "grid_values = {'class_weight':['balanced', {1:2},{1:3},{1:4},{1:5},{1:10},{1:20},{1:50}]}\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "for i, eval_metric in enumerate(('precision','recall', 'f1','roc_auc')):\n",
    "    grid_clf_custom = GridSearchCV(clf, param_grid=grid_values, scoring=eval_metric)\n",
    "    grid_clf_custom.fit(X_twovar_train, y_train)\n",
    "    print('Grid best parameter (max. {0}): {1}'\n",
    "          .format(eval_metric, grid_clf_custom.best_params_))\n",
    "    print('Grid best score ({0}): {1}'\n",
    "          .format(eval_metric, grid_clf_custom.best_score_))\n",
    "    plt.subplots_adjust(wspace=0.3, hspace=0.3)\n",
    "    plot_class_regions_for_classifier_subplot(grid_clf_custom, X_twovar_test, y_test, None,\n",
    "                                             None, None,  plt.subplot(2, 2, i+1))\n",
    "    \n",
    "    plt.title(eval_metric+'-oriented SVC')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision-recall curve for the default SVC classifier (with balanced class weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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L+NeHXuu6qzwXp4dSeDNKINatWpN8X5MHljdzgyvZ2P2pJwHWrkoh+12+S96Ie5VMLvDw8qZUhHHJu57E3YK5lmk05o6ddh4440LUkZK4kG7sAUFb+G1mXljf4/0TiO9vcOynWBuYdT65PG2LVIIrwsZpGX3+Xm0tbFF6DuJewB6d7Td32Foy2M70hma8GyYlytpPxckkucfyrsPSXaFi02+H2Jc0DdHs2Hy4wnzC90mY+9OvOBbzUzc70AvQdt9u4F3XFfsRZRNpDD5MRQP/501mPgnoo3h4eh78LN0qqEfTEjHJg8DRR6ZfDfYN2igSLeft8sUAREQAREo3wSCD6DyXQWVXgREQAREoDwRQKgzEoGTCV1MuGSqP9c2CLrP/AKXHH8YOPZh8HUF0W0gdRID5csRPM5PlRz+drxPi9D/T8KacL+QYtDejjDe7fPijj/xAdeGCcdjyffEMO+vWXHfLmHl2seEsYmFH9Cbou1YCjtW4tMwTlqDFZRdCNAoDPQlrNsnrDQ1Adc7DDxnM2i8ELutLL0G04TqUWWTUzZItRXH1yLk+2/UE/tX6Je4P2fj/wT2GgjD7b5ejA4U99oEvIGdvA7j3n0bennD2tjjVj/MrujmU6dONT5+YgVryRV5mwDEC3pMqGllT1QHhg6vEL4z5V4ftTduNphPVB1Ch7lbbrnll9yb4BLhRw/ieeHjy+zeOT1h9853BHyZ+hojW/lowr++mLugV6CLVeEky60+IPndyL2JX+P+PXoAeZgAzu5hgQ+bziTsafjPo720pr2Mi4S19N7gPg8hjAlIKHKl0zG/sTAcY/E3xt+0p0AwbW74/81Onp/Nnq2izZgwLWh32ca18Bx9NzmXeBnE2Zow9lydQjt+KSH8+/D+CbcHzZ3y34yg/Ynovcf/ULx2N3/U+OrVq+/z008/mSAxqt6D8yQ434Wj9Y8mNP0wGqA4ex6f/2hWJriyXYEdaevvRz2wW90rYZ4bunfBfDO0Bwb++ejzHiJ/uwem/sc9+D/MeKeP/Wv0M/C7m3DWn23Me6EXZkd0XDGhbM9Es9DB7sMeCffhS56xp3G39p6JEC6edmlYKMOjpFsvTPsF+NuztzKSl/WpL1LvBwh7Gfbq1PsxTC+AjASNW+1ejeNZtx2Y/8RdnfuWug+gH7N+2Z7njRBm2kTrlRaGvE0gPoX7ac+8V1NyfdYtAdrVaejpvAfbTJgwYbpPFPMz+pT18OtubvQLr2Ks4HpP7v1ocwvVN4SbiPvb4bUJqnt6z+JMJrJinXeKgNbf0haegq1/F/5f4hFW8LDjyJ4IkyigLIdSxM8SkvyIcNbXf4xugT6Ge3YS4V6OhuOe3M21PU+m7qNvvypmjf8ORYA/gPw+Rq8fd83SQr4TLApp2cSijz0n2b2Eb2cC2DNsahraduSMD65W/7zPBMKr3KePcKpG2a4m3vOU3743kql2OFahv2rFO+TPZAEycYP1pYTbMAz7GvU6AXu0X7DvqFdh/w5mR/RGlO0izFvQccV7fH3KbvenMnopTA7k2fgiHoBnA/sA7tlzxD8Ve30mDW7D9P2eHUmVj34ukmXerN3g9Cr37ERSjLIJMuC+7UCdjg9zm8s92SfJf+cNo+4jCBe8+0jvRsL3D+PEDZj0JMy2ocOD5Gl9UlQNIR3bfTcIR5Kp9D+u7YjF+fQBz9H32HdmDdw7NmHyNaFPiKcD6224sD7K1C/kE3yPxC71KwIiIAIiUJ4J2ItYSgREQAREQATWKAEGgyaknRVmagPyM9EjGHiMR/dj0HI2g+ftQ/+MjVDgMCyMUIuBpg0mUyoGSvEBJoFMUL4iMTBhtvNu2G3AW2YVAzsTzgUKxlcmTH4E7gzmpmD5XyxU8BtlEHEuaoXnXFyfhsO7NrhMmPyIR8D/YX9BuAO8PWKakCZQCMH8CmPvFDcZuD5J/P/gYO3lhbgHFvLwacwkXOLkhw+6AoHdvwl7C+mcgx7pPfJlwvkD0gqkPKS/f5J0rX23N3fK8Rk6ENpgtjO3qLKjgLhuaW6k9YE/TgtBWV2cjIGpaQgCzokKwGPOsV/ur7EPngHS2JHnqUPUP52ddG2iZnMLQ9w/+N+dO5OEt7PUTTA0Dl0riX8yp9o4Xpww+eHDrcJiz16g4BJ/3rxbvkzrH0wIl6vmeZqTr7IkpAPuSgO5d4EAKMHPhMOP4DYhdN+soKCg0DNFG1xJ/H74D4Lf/QlC93hyTCL24SJoq5j72o6FuGcGljw+/9HcanLxIMLMxMmPIAx5PukDU7cignfqXaI+L9xZdmKYx0hYW3rWJoso+pnrcBwbehyeuAuEspweiXRzsvtgfTL3qxvhkuYRiV+qVgTIduSTTTqamoNg8kJM3zYCR/9De7OJir/tmji7Edd2xaRUcLgqYfIjCBu+jyytQBHuZCzWP5aWqk77uS6ZoJP3wsuRTK1/upP7G538CLxpl4OxzAvDNrfjmkJ7iQ0mPx4xnpYQLEygb5NqhRTul+Ng5TN1E0X8LGYt/Eu7nUFaF3hX7IWEw+H31O6h/zzSvcmHjZr0kTaRHv0+iHrn3U45rogk+i/qMT5yHbfSnr7k4tHQgUeo8vlxz6KWjXHqVZLJjzBJ263wHHZ7x9+HTvrMEsb650DR3gr1zeZYu3Zt6xeC9yrmU9QlOvlhQbyK9j2nRXeJwsn8AkXd8/5t59POwbRJn+7ES8qGsto3wuOUfyDmw0kmP4IsadcDsAR9DOY2diRh4BH+hN9A/lt1Nv8BaH1xEUU6Nrn8XuhRj/sRTL7Yuxt3m+g0VZVvnWhfHXNd/XuKtxKnv7fLFAEREAERKP8Eqpb/KqgGIiACIiAC5Y0Ag+yfWBm2B0KIZxhg7BUpfxPsTXDrirZVbzYgGoYehLBgYDKBUiRuYGWg1Ye4wSAUuw2Y/KC5UFAmWeyIphNDx+WEfbxQgNUXNpj2aoa3lDWzCSvaqE/bsFzTYfx5qjJy5EQ/VsP9RnibhJqSKlyiOwKa53EznVZxr8aQhw+zpbdEzMXejuDNVtr97K8TTQa0hVZTRvwtDROe1jVBZPRPLyNhHMLuqVz/J+qWTzucZ9JOTWhkwqV90ZXRcUEigqcdGYTbxIYpE+LshN6b9tYes5BiMqg99yRww3zXe4YTE14I9gq7kBZ4v2QmcZ8m/WDiA/MownyQLFyiG2Gjwps3/ARMYjgEOEuo88O4Zyoom4Zg683EdPw15f3d2zE3j9jXGSttpH+aylp7GoQOJsHgtR/2ePugDZo9fo09qeK+zeO+TcdzC3Q1/nB3E0x7PjJSeXz+C+VH3VP1vY7+4Tf6qiA87dM/R8F1Pvo8JoWOJjF7Zk09g44/u4FL4R+bIDeB6E3mzPvLni0TjDr+H6IWRyT5/ncFK47fMvdkij5pDPfB+ud9kvmvCTdYdvL50J5eQzBpk9tJlf3nFe9LO2ItEDoTtyMBv0oa2LkZ9NnvpPCzybxh1H0+/nXQW5Du9oQfnSp8Cd2X8h56LVka3J+x9LdxL+7lC/GLwhZrD+PRu6CrcI/rYnpBLdbcFJNI58PzrDD2T+HuoSKJ8Wx0grd3f8pbkpk8n7bDcxJ+Jjzem8majfwfc5OXTW579QHI7R4kVTxzT8HjhqSeeXREyL0Fz98eYZIF3JOX0yUPi+cRqF8ahrE2mFJR39dTemboAc8HCWo6rYLVGMoWhOFeJfveiT9rBEpZLu7JX7SLo0hjCenN3HDDDVfw7Ll89HNpK1Ayzx9TTVpZsvh9g2G6OLWKeo/jvgV9PM+tcbS2HCi+KQ/Bb/3w8v10/8XD/biGdmXHqs5E27MbKNqOHcV1Wnh5Fua9oT3RsIlZUzwKy+2dICUCIiACIlBBCKz+8qsgFVI1REAEREAEygcBhHG2gnxvBuwHMvCxo46O4HrDhNLbYMgGIycvWbKkgLBPIgzu4Qf1CWGDS1bbvYEwYRoXJkhtQ5ydGYQVWfnPAPNU8q0VpvGWDT5De6LhBc+2SnNhomdZuUaYYwL4SmF5fkpXrnBFbEqBdLq4iX7w3YQBZz0GrNW4N0H+XhgQho1JLwtH/JDLY80Jpr0Z9DckTt/wyKPCIVNcEc+OOjgeb7KvMhxB2rXc99f4Q981fo8oi50NbvzrUpdWCE5G+GLj7gVPM3AfBa9P8LsSHfwPiAkXfVjM+AQEA/gh3p3044JS7GO9eyqTtvAt8b33zt6SgdnMh+F+/ODtyUzqNYSyZDoB8h1pxGZ2kiVW+A+h6yUPUrFdYZlKoBxUHH/7z5fAjtmiOBo77bRTPY7s2ohwNfxzaXFoF/H7QDrVi0unOP8cn/94stRrIe+C3+IOCRZW7tqkTeBKeb0ALLjOU5+X1bNFGb6lzL6U8WcL4W1zHL3HH7bi2AdKZpKO/d9GPO9kYUrZzSaeA0U50rY9C0R57Rn2Kl5v7xAxv8Ue73wi7t5qkuJf0MFkEe3Rbm5pTYCM4T4s9hlHTSZ85vt2hft8JqUmRf0T7PP9NZNxNb09V5N87V3xQBh/Dn3tscneW/afB9yblj4c3zHFLsAg/LfcK5sAqQTbVpjDLT5uO5gZqpHeksy0xQSU0dpv3WT++XLjm6ENQukgOcr9Y6p75fNDGD0C/ta2qlCfra2PS7UohrRH+Xh5NCs1adJkM4Txdl/i3zUmrPf1IK9kfeoekTL8GLEXsfKN8E6iY576ucRk83WdNedw8dGmtPuaCe+m9XyhqHMhjtzvOEPsaRnaAivSKfINzDf+cNq1jTuaolvacxhO0PhsHd9v9m6158bUkGS7x2Je+hUBERABESiPBDQBUh7vmsosAiIgAhWIAAOQoVRnqB3HMm/ePPsjXBMKmd4bvTnaK5usuJTB5lEMUo6NCpl9ADPtaCAGWLbS6/rQvRvmJaE9bpCP7Q4JFGH7eHuiSbi5+HvnDb2lDJpNImX6K2LPt7UyfI+HSVcS3he9gQnbTQBgZiaKQW9fhBknkEY7wtuZzLcwiXEz6do50MNxG7pgwYLhyYRCPn3yu5r8rI3YavYGxHuGya++DGq/IL3h+L2PYPVr/GJLM33EUjAZyNufk/v2tj9ZxCdAsAeTGvh/hN0akk2AmFmdP4LdC9MmgwJFHTqE1l8Tju+IryolzP3U8f4wXFIjeh8Iv1XSQMkd4/lQ3snJg8Rcw8mcJVwVElSkiDMrhXvgzL0EYfw2VUoXtoL6LaMfLG4nVtR/s2Qc2FVnf7p8Hve8HRPGm3ihXLQ9JIuXpVuJn/9ofpTV2ka8g436hfZ4w0jil48+L97mKcsbPFtJsknpFH+26M+iQtDovUoamXtj/8eS1G8NOcbLTjkmFJcnYSbSJ/hgDbwliTkuiVuiU/z9BIf6iZ55vJ6dJq1ou5qbJpxN0q/09wozDiFdnFR+tC/bdfUq2vpNklt1ergYpEgUhMD2bvP51SVusQ3Gl9MSo0+N32Mu4+2c+hTbPgk/CV2qEyDc+2j5im2DNkECg+m+LkzwWp2S3eMFxU2mEC8jZUee8c15BoFtwcye8F0/8q4K0vD9bLIEQ2H/xt6Pfn6qt2dhNomEjT87Ebe1af0nk8y5bzaBYd/hB8IwaIvGLYt3Uz4Y2PPzJPo2tD18Z2F8Y3avuMe24CpQ+PfzdpkiIAIiIAIVg4Df8l0xaqNaiIAIiIAIlFsCNnGBUPUrBoj/Qx+P3oIBUjMGJFcyEPk5UrHGuL1jA8uIWyErA9a+OHgBR6GzlC0ggzFbwepXwI4l3w/MPYWKrrqMCxFShF1rzjCJ88C+sDQKgjCgNuze4X68TPod0Rvkko/9kTwD30OJezPaC8crk+5uXF+FfofJjBlMdNkfbttq2SLKVuwiiLBB9VPoZWEAW517AOn0pO18Qdyp6F4IhhuG/qVibLDBBl+QcLBKmHz3j2RiK1WDa8xh5k67tvYUrHjHrZ0PS3u2tuWlr0O8u5ncz5LsisjmHtkkY6BgW+DtKUwTJqQVHEbi+Wcx4iRrhMCCiD2plbYSfabj9ykMXIV23p+29y7hjsfNhKx5V/l6/hMKtiLhOuPLPPV5eXm2KEttX3DuQXHPjgnfir3nPr1SMuOT+ZmUJSFMuj4l6AeLKXO8LcMtsS0XEzVzb9LOtG1lGi7zzJOHrILzC2gv+O/Jt0eRFf8+Kn1wSdqmJRO/T9m2T+KuifaZVRsMuUTLFa9f6OeNZJMi3i9jk/5uc74lPyPCY+gOPAPrZxx5dcD4dxlOi9FZvwu5d/E0sMefndVZrD0bTIplzbfNrZTwK3QXdK7f0HlhAL/+lCF43rF3bty4cQ2uo8pPgMzgu25g1EN2ERABERCB8k+gavmvgmogAiIgAiJQUQmwlf136mb6AYThlzJgsZXvldBbMvA6A/NhdBFlf7SIQHAQHkei666//vonYj4dCdjN20mnL3YT5iZV+Ee327dNGqhsOMbrQJkTB3V5KSHCgN4kdFiYmE06PIiQZgBHQkzEPssmNjADBf94ebxb1GTVq+0e6IGQ4b+snj6GMh/B9cFoL7ytyf0+gesTSOteJg66Yy8kPCAN26XQlQmOG1j5fjxlOZw47XDz9d8M+6UIhs8ljbNI4yWu865s8o72OYy8j6Ye+/kMcGuDPRDyULbh3p0wHxF2B67bezfMDt5O2He93UzCx+uNvTsrgwdH/dPZYRsM9tOFifjFF8ZQvnieEf9EayZhEuOstWv7I2DqVS3XArDieDIri+fkGj9NvGLvEfedolvXF6hC3BEw9cD/zNAP66p+6Kdp92M5mmlmdDU0z8FEwjUKw2Zl5PP5zyrj1IHjfQz19c986tDJfeIs4Xs6zEYmD1bUFR6LIq7xm0M68XJF/AtZKW/VyP0s5LeGLuJlpD+Jlz1V3vSvNkHtvePMvIM3qdNyb09lEiaeFmkW2/ZTpVPe3Hn2TBB8UFjuQbyPbk5XBzhFOU/kHhyVLnyiH2ynejfs0Xscv5HeP4m5JmQE0XJEy5ekOHGnTN5RUW7xiFlaKvF820IPe4ebWoC+h3fz27CcvNVWW83y/5HFe6Ux7hOCUAk/9CerIrsc1sPbyp9t+eKcyDvXfi6hZPm5TGijRRLl3XQOZb4+4vEGcR6Fyc98l8+O/pcHYYcTtl0kbNxKHLziGHJmYEfdks8g0rJnqS5HqnXCfNEy4vncGaO52cnvefuuM7uUCIiACIhAxSGwJj5uKg4t1UQEREAERGBtEVjFSsleDFB2pQBdw0Lsj5l0AsT8GeD0YRBjEyCmzkU/bZZwxddpZkctIUy/mDX5LwPYTxjces82CNs3RfD+j3coQ+bcSFk2idjzYmXQaH9O77lZmp0Q4NgkUzJlK10zUkyamGDhuVBXRpiwK7wP5fp0dAu0qauZTJhEG3gwdln4N5wIeQDXB+z+MqjdF7vtUOmCNha2M+Q52s8oypyxgJM4GSva0XsEPhq9Gfk0J59RuB0QJvAPZfzFJ4b7x9gvRO9p5TUBNWw7mD9+C5m0MP+oit7beex++TnqmUf7Qp8W7T6T1a6lekSKL0u+TNqV3aNGuabH0W1nEbd/rvHTxIvvHkgVxu4HbSTwpo3YMxMo25WBoO4Kf43fxbQ9m6hMpTJ+NqMJlNbzH80jB3v0ubDnPBcVTwN2M3J9trg/Bf7+UIhMnp34iuZcCp2HONGJvGLLApvakfrFmSWWg2es2LZMOvFdH9gLEtOoiNe8E0zQ+q+wbuNZNGDvt7hEN1mdYR7lXCPXthmmHecM8zLRPinHHOroq15sGwwDxttXAh+fTl5MvkPa0Zb3CxNbjL0d7/DvfeIcUemt9k/ZVXj+49dRy1wUu1m9UxXSrcfCnpneIUMz2g5y7ecyzIpVGe3bV+X40YzDpwlosxbxyQ/sd/MN55+BItHwT/luwi+fDJ4g86PCApyJGUyAYHYO3ez4uH7eLlMEREAERKDiEEj+tq449VNNREAEREAEyiaBSvYHn9kWjUHQRz4Og9+0wlcGWoMJOykMvx8CiG3MjnD8GIwgLmm8irBwRhgmqcFg1YTNo0LPasQ5L2nAYhwR4N9AGZ5u0aLFdsUEzcmbco2PRLSdD3lVDAjbk6CXVnwOt1STH446BqxzKMBKeH9H2rejW3K/b/BpUD+z+/y9cxHTJhOI+wH6KgTWTQjwZhjIjqOKp1ckYgkdWJ1rwvVAkU+70HpAaA4PzcCgLh+H18jBqu8V2juYaW08umI/9ItLW4jrj8kKvfJqxJ8F7vcW6VJmIrAp/jmvxEyX9jroV4O+Ia1gi3bR0HPBHpdOIXzbHXcvYZvMCtc+PlyiueWWW5rgs36ieybXa+j5z6Qo8TD56PNII/5ssSo552eLuH/HC1b4v6sizqutCEzt+VmbapLPnHtbbFloc9E+PR7Xp+FNeDby9jTmlt6PdKd4e0U1EXpbu3oKXQm9CH1cqj/vxi+uFi5caJyXhw6b0+emOvIpHieNJd4+uUebpwlnXlbOJsWEKbE35bD6eVVsG0zov1byfp/sI+fb5Jno4NOknK9FJz+8uzfxjz4b3jkww/8xm+4d6Sey/jYj/RJ/25HGKl8GzMoRexHrpEmTNi7imIMDk+aNiebb0RL6vFuLScaHTRYszoA+I2uG0QS32GKLd7ieFrodxPNp9a2E9gt8RjB+GBH6yxABERABEahABLQDpALdTFVFBERABMo6ASYB9mQgdjvltP96sAHIDujowIzL1Iq4JsALFIOg+IDeuyWYtm2jL/o2cyfuCRh3o0+xa1Ok0TtmS/tLsFX3Ez8QLGL+CwH/s/YfFGljRTypdwvi3YhTNQbAnRFkNGdAHRe8RYLmbOWomx8QCBhLG8jtxMrwatEjqaIJ47cRglP7rw1TMxCa3h+zpv6l/FGhyW+pQ3K+w8qVx6Xzz9DPdv3chmC4K+G3RW9GuetTJz9wLTYZ210C60sYeNukl7WBnYqNlGMA2sMYyjqR6I3Jb3/y7Y99H7Sp4cFv+GPHMBB2LJfb0rbaEXYM9kCIwvWQaFizU+6vcffOB3lLKZhWpgMsXfJsmS596mi7dMqVYlKscVktMLzbULYi9z5S3lbeTlv41dt51jYnrr8cjSXeULyjN1mJbKte1/PX2Zjksaaf/2KLl6c+72syOjXMzJ6tpLvMiisM/elvTK77YE1MWBsKP71bIZN72LaQw5q/+IosTwqztUnYR9IVgfLu4f2xf+PtSUybkEupbHX51KlTbXI7CEP7HZUycAXwaNWqVa3Fixe/RlWCyQueo/Po/0dmUrXJkycv4j1hCzB2sfD0uQdivG72bBX5/uqZY6bt2/le2Zn0bddkqSrq8zX33+dh3yy1wx2h3q2QyZFJ1rZ8Z/dburCFIuZ2Ee/vKGPa7x3qcZxnmyKr73DvaH7cB+vnU6bH/e5GmEYWlj7lUXaa/JmPfo40FrFIw5I1VSdmJP/lSLy0z3DyWEVd+dbdHDbewybn5/uLRNPGBrg1SHT31zD5zjMOGXqvIiYTGq0Je2zo8Q35+kUwgZMdXQbnp7j4F7oq9/d4vsHs3RksMiBuvyCgfkRABERABCocgfhbqcLVTBUSAREQAREocwQYDP1JofZH2w6M5qwQs0mJjBUDHy+wsYHksOIicjTME4RZZuEY1JxkA2ysh4XXPyMQ/dTsxSkEW/3I7/swXB0Gdq/YJEJx8cyfwVhj4r6NNZCOYX8m35Mfls+ECRNsleHnZkfVZMCbki1cjoDH9aHeOxYl/S/lXhQJkXKFYJMmTeqT7tWRsK5169ZxoSsDzZYMPu04s08Y9GYyUTLXp7VkyZKFZifeEbSdJ9HfkV4waPVhEk12WMyJuAXxI9d5tcLo3TDBfREimFAxECLBo0hbxe3jMOx+PBf7+IIgpBji7d6cP3/++9h9PVpR53beL5kJl3Pg+wuc7sLeJFmYZG6U6YuI+5EReyErbd/a8mWFHNfQBYyrrqGs1nQ2XVJlaEJj/AIBmoXhPg31YTN9Lhs2bGht8WYfz0zixp/LqHsye6b5FPf8J0s7V7d89HkIGU04vcLKQB07Nm3adNt05eGZ6smz9S26B2E382HZtWXP50/h9Xq1atU63PslmsQ1AbPptaaoqwnSvfS5U7r3Wbhbs1NY2FUIDN9IU/CG9DkdUvkzKXQw7Xf90J+/6xr/R6qwlLHcP+tMfjxO/fyEw0MIY59NVd8U7gO8O9zS9rnWL9O2RqJfRxfqT/hm+cSnA9dDGxf982fvbeaZ0YvSsttRUNRpeJh+Db5LOqfLi/dkV+8ftl9/mXeTcmX0vcO7eAfCxstFQZL1qfaseVXovnhHM+2eUK/7Sc++zbpzz4IJg3z0c3Xr1p1NFsvD/OrSVuITPKFb3KAM58QvSmAhnSjDdN/LJo+6M5oVdU/kaN9FPr396ZO2joaP2ol7UcjQOCadVOE764lInJNpW6eF18so9/MRP1lFQAREQAQqEAFNgFSgm6mqiIAIiEBZJ8Dg/y/K2MuXk8GJCbHjkxrePdEMB/a9CO8Fv5MQbL+UGC7xOhw4emFNa4RdVxKmhoVjkPNoYvhU16w0XMoA6RT8Tchlqg2D9U8pe1xwHXMu/Mvkx94Ii0zQvU3o8x3C7EsKhwomSdojsFgV6omJ/llc/9eHpX53U774USPePRR09fDXMH3M24sxR0b8O0SFf97d8mMXymCu/0ZP9u5z5sxp5O1MDCzDbgz2Rd+ZbgKD9FoRZhe0qV+ZOJpnFuq2JeU+C70b9+VB0qhu7skU9+mMiHtUwB9xzo+Ve/2epUS5tsZ+fJjqdHay/JaYA3WwdmF1aYuxX+g/AaHQ76E9bthKcsI97B2oc79k99b8aUN7kP89WHcgzmXYl/p4xZmEt4m6JWG4VqQVZeejV+I5eoALa9MF3rE0Tcrlnztju11p5rWW0qZaqzrDOz7JES3HlClTrN9qGLqN4zn40vvDJvpc7siz4IWtPogzAXbNmjVNCFcXPcJ7ICiKP5feLY0ZzSfn5z9N+rl6lajPsxXWsH8xzLwqgrHnYbhBssIw+XEkvLvj1xp9waJFixYkhHvBXxOuJ89okZXWtiOAMH3Rq3zYbE3aSX+0f1/0yDa+heddPIF62+SPKfsfmT6YlYOrwj+VYWJ9j7UdU2/CbGzMmvTXJkh6Icz14eOBwiOMooLOIpMBFelZ5/5fQeXtu8HU53Xq1LkqZs38F5b2fp5jMWBj3wnXpYhdhXto7cremZ3Qe0TD0Wd8z/Xo0G0T3tP/jvp7O2Xeh3wu5Drn9unTytCMP7+Etx2f/lupUHTcbULRv4/s/3asvZamivd38Dgm2YQRZWrOu/gdCmELT+y7xtSm4WRz7IrfGjVqWL9g376mDibemTFr4V/uyZ3UK5gcxHw5nFT1geKcKE/W33bhH3r/6hNjgUzSSQ76uLMIY+1nsQ+bq0l7HEVc//1Rj371kMS0jCs8nqROe+IXfBNZGK4LvZvor+bDxNq3qaqweszGBbHL1b+U33aZnxa6zCec79tXB8JmO3YxfH7tsPtxyEAWRs0oFFgXIiACIiACFYZAuV9ZU2HuhCoiAiIgAusIAQYk1zEw2oHqHoa2P1Z9iQGQDcZfY7D/AwPK6bgtZRCzCeZmmLsRvjP+W6JN2Uq2k3788ceMhK/E7006J1pEzBvNRBUgxH0mZs3s1wZMDOD2QRg0mHRs9VkLzE8o+zDMN7gehTmD8tfCbIl5HPU5GPdKlgNuw6nHsemORbFwJVEM3N6gPC+Thg3mGpDnd1zfQ1m+pCyVYdGaMlyN31ZhPi8inH8/tKc1GIB+TFoTCNQEvSEchjPYvIW0xyBI3Yjrg8jvfPxqks/e2E3QFQhtmfS4k7D3UIaZJuAnnSfxO4dw26G/5/pp/D4irem4E3XVZtgPwezKdRW0DYh7mGmKNvQU9TChcAt0J8J+j+DmKcoxgusZ5FeD9KyOx6KDe4+5gDTiQgSu867Ifyh5ryBhK7MX1gxPlhFl/hKrxuUAAEAASURBVIgyWmVN4HG6haF87yYLa26kbaztmTHhq/0h/UiYPoD5CexNIGT1PQJtaVVHm+pJm5gSsxb/awN/7sVDhLQ2YuoJrttgvkVZZ5G/TT5cQl77UtZbMbtwbQLdUlXkO85YhepYynQ7dhOuNKTMZi/vygRTf6Dt+X0MtgO539MxN0HbsxwXVnF9Lder0IEyQTZxPuFiP3RVWL3LdQ/sIwlbh3tk7heg66OPQx+J3gVt6j88N4vIazYCUitDSpWv578Y4XnK/FN55KPPY4ffZfQn+5KHCd12h+GPMHwA+zfwW8Z1I9rfifA0fpXR9txemdiX0y89SDqX4G3vKlsZ/jV878H+I4zXI53W7Ai4gutt8HuA9My+1hTvwEsos7UPaxsnUtaGmI+gR1Ff68NsEvU8zLZoU9MJb8LxdOoZ4pwI0x9Iz46ONIZL0S2JdA3aTFN/sYjh/ph19S+cxsEpcCDuhdyHuVxMJv6GvKseXB2ybNvCxQ92761fX0i9buY/PZpxfGaxBeeop9GhwNrxXP4Dx/Oo/0uWFPp2mFj/2x+7vY83wM8SvQht31WmJnP/esashX5t8iSY9CL+jaTblLjPEXYq7pvi1pFr6ysmoceiO6JLVXFP36E+/cmkK3pT9Ne81+6jLB9TLntnN0Db0X1nof2zdynxMn6vES9rxXP8Om3Y2ucG6Ca01Q8pp31L/cG93By3I9BnoxfzHJ3Fe/9D7E3R63H//gvbfqTxt+1wsm9Vvh0vIK5NQttz1c/uIdevwHsm140xz8G097upf7i2fj6u8tHPkdhT6HstUZjeDOcN+HYYTH3m4NQQN5s4OBndH70T2r41clb2X2bU09qtfSfYEW4vwqUn+X1JXjZ5sQemtbemmFdwv+tg7m9hUVcR1t77s7nXI8yBeDfB+XCs28PnELh/QfrWx/xGuHqYB+B3KTr4/iHMNSxemsV1KvU4Hpaf3ZMNLRBx+pkpJQIiIAIiUDEJaAKkYt5X1UoEREAEyiwB201B4Y5kcPNvG6Bgr4Pe2TQDJIxgcFbIDC5iP0MYsF0Wrt6KOKe2IrgbxiDJVj42Q68XhnyRgakJVrJSJiREsLEb5b6JiDZws/QOCAdeNqgM0jOTMIGdnxlc38YAthf2uBTXeyYx4xGT+BXrhICqCwPvRQQ8E20D9XsZHAbxImWysj7PgNIG8JmqFQQ8Az0EXQtt/2vyvKVp9y1Mez7uHWH+Lff3BdwO5Nr8jifs8dy7J7g8l/JcTJza2G2wbUKPq7n2QveAY5geXm4R9qvgN8AuTFkbQpB0OOV/g8td0CZsvMu3H19fCxuqKfidxOTLBO9QGqa1Ker9NWXZi/RNcGJ1H25moqIsE2mXf+K+FToYfGMa26SKtreElfwHIgB4ngAmmDLh+C1ozz4az56xm2B2Z9QxEzvt59+0n5aENWGMfSeaQOFSzxa7qb7c45sofyDYiDmV3i+sfiKvz8hhH7Q9cybI86rcT4DQRlaZEA32g6nURVxelMDb6rqKe30T3F/xFfcm7t2I8wnX9iw1QD9mfriZYcqO9TiDuDbBYs/oOYErkyaE+ZS8hnJ9UOiWyliBR16e/1QZ5Ope0j7PhGRwsbZlwuE90I3Q/0PH+yJ7zkJl/EwA+4J38Cbp2H8OHQnP93DbBN0cvk+Yf+R+riSt63D/GecrzK+EKn6Ts03Hdkiyk29f+uU3iWt9qPVbpuPvMrOH6kfMY6njNO+QwrQ+1oTV/UjPjvNJFswm945J9kfgCJrfpf+ZSKTGaPs2uA1t5ZmE8aDZy4PiHXQI5QzG2TBYn/v/Lu+rjIpeUFDQhIATfWCe2wG8V0hmlQls7V3REXuqyYkfyPtEmzjx8b3J+8COxrqJ6x7oSqRxKuapkbbJpZuCu72Xr4+0eXMvNcXOoG7scltEfvZNtTHmrZZZQrnMqQB9Mc/eU3ZRmsp2X8D8XFjY+7Yqpj0Xtkgn+mxMxb2jTXKE3zs3hGUK+nCeqxu5DurC/RhImFMJ3xc3u4f2HXRuGD5qjOfiOMJPjjqavaT9HDuQHmQHsk2Ad0BXgXN3ytA9ep+xDyTMxfxf1MeWZ0kV6du3nbHbFl2P+t+XcF+tg7iONv4A39bbE74H11XQtsDoQ8ozDrvFdfZ9Rf96IPFf5dJ2uu6G+TRm9J5Y0GXoa2knj9pFKsWE5CtMVlmf4r+/pjVo0GAI8VJFkbsIiIAIiEA5JxCTNJXzSqj4IiACIiAC5Y7ACgY8t7ANf2tK3hX9LAOd7zFnopegTVJgExQmTHkHbYP2FgzgD89m8oM4gSLtPt5uZuJ11K84O8LYmZTjMgZq25CODXTfIs5o9Cy0L/dv2J/F77QFCxY0oq73c70SnYlamkmgVGFscoDydcV/f/QTlMFGcya0W0J5/8B8AfMAynSaCdW5zliR7qfEbU2E/mFaNtBchP6FfG5BcLUdYT6wBEm/P8bN6Iloy8cG9l+hbVXrEsKdgtXKaAIBE67NQS9HW/1tF9BwtAkQtmNAWuj+4WZHGExCcLI71s7oAWE9TUBinK1MkyjjQMzzuAfbc98+x17qinK8G82EMgyPXifYP4pcG8sPI9dFrCYAgNsRpHkgnk+gf0fPQxs3a3+2svI2BATNCJf15Afxg8kl4nYkHROSD0PPQFv6JlB7E/cD8D8fuwlyK2EGintvbb/UFOkfT+IvoE0Aa+1pCjrlhBF+5UZxv5aaMBqBzD4U+ir0l2jjbc/CX+iX0e2sz8QsonAfjeOuaNu9Mw5tfJbYM4G2FbLNCPIsbo57N5Tri8xqYdB/ch1thzglV8TNy/OfPPXcXfPR51G3Kei2lOI4eJjQ0/or60/sufwnZPRvhNjbEu4p3JIq+jbbxdgczzvQP6Hno60/sneZvef2517cFbpjlFhZG8lZ2Y4c6tOKBM5Av4H+E72Yci7EnIQegD6FMLuijUmxinC26ns30uiFOQq9INS2y+hOGLaCwbfYiyi7l7xbD8LD3vv2PbAYPYl2PBRznVXweoXJoSYwvRYWwwExHW0Tm3afrG0NwH4i7HenDVofkFTh35Nw++H5Inoy2trPbPQIdE/Y78r7dgRt2NrtGlH2p9TkeRF5tybDh9G/oO37z569v9GfUucb8W9C+VM+e4TLq4K5tX3rk8209469B21Xin2r/h+T1i2MFXbHpNXtGNb/2nvJ2uxvMLTvmrgivZeZxGhO/P/g+DXa+niro/8vlEt5B+xIHUfiVkSVtJ+zXUVMghwKy8tI3L6H7JtrBXouZfoIfTplzOsuZdriP7Rbm1Q2Pvb8Gxtrc9a3PMEkUSvqeyd2xzfa75TtJLRNDls4myh93/y8Ir3JhN+LMKfhZt8j9k1rYa2v/pXrXvQvOxDmf1ynVZMnT7bFNdbXB4q4z1pb9NcyRUAEREAEKh6BShWvSqqRCIiACIiACJRPAqzONOHTdAZvbcpnDVTqdYkA7XUW9a1ndUZ4sAHCkzUmNFuXOKuuIpBIgGfvQNw+QF/K+8IEr2tNUZYeZG6LFEzdTHl6BDb9iIAIiEAZJsAxYI/w7XJhWERbZDWqDBdXRRMBERABESghAe0AKSFARRcBERABERCBfBBga/8GpLMFOuXqzXzkozREIB8EODpnM9IJJj8w52jyIx9UlYYIZEygWRhS74uMkSmgCIiACMQIcKTohkx+dAl5DNPkh1qGCIiACFR8AlUrfhVVQxEQAREQAREo+wQ4LqEjpbRzmb8o+6VVCSsigZYtW1bjqKnrqJsJV5vQFg9JNbHB0RXHRBiozUZgyCoCpU2Ao1uO5vlcwXvDjtKREgEREAERyIIAx5FdTz9aO4xyTxZRFVQEREAERKCcEtAOkHJ641RsERABERCBCkfgCmq0nPOLX6lwNVOFygUBO2Ocgh6L7oxui3Dg5mQF58ibBrjH/RDEPpksnNxEQATyT4Ddgi1tcpKU37f/pMp/DkpRBERABCouAY6+OpLvG/u/LVOfsvtjcMyqXxEQAREQgYpMQDtAKvLdVd1EQAREQATKBYFmzZptyWBsCIXtbX/yWC4KXU4L2bhx47o1a9ZsmK/ir7/++qPtz0Xzld7aTgfBqv3J7iDKYf8TdyWTHS1w64fbBMxq/BHt3rib4GBztKkP2SXyWsyqXxEQgdImwK6PRuTRE/16aeel9EVABESgvBNg0njTqlWrbs4Co83pP4/je+Y86lSZb5qFuJ1f3uun8ouACIiACGRGQBMgmXFSKBEQAREQAREoNQIIkP8i8R6lloESjhPg2INODHj7xR1KaCkoKGhCEhNLmEyZiU5bHMLqyHMQDDxCoWqgD0NYcJgVENP+7NysXn0Az5O5WOkdZIqACJQuAVYr2wSlaSkREAEREIFiCPDdcgSLN/rZ94t9x4RqMW4nsejoV+8gUwREQAREoGIT0BFYFfv+qnYiIAIiIAIiIAIikBWBMWPG9OM/PpoT6U70N+g56BXo+eixCBCewzwCQezBHJs1C7uUCIiACIiACIiACJQ5Auz6KKBQdsSnzX78jX4BvSvfOu9gSomACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACKzzBCqt8wQEQATSEFi6dMiqNN4l8lq2bLm7555n3CuvDHSXX77QHXWUc9WqFU1y2jTn+vVz7vXXnbvqKudOOMG5qlVj4V5+uYp74YXGbsiQXq5KlSpFI+fZZdq0We7ggy+gzPNcgwbZJ963b1U3evTe7tFH/5195FKOsWrVKnf11fe7v/76xN1770K34YbJMySYmzTJuQkTnCsocO6ll6oTdjf3xBM3rJF7kFiqqlUru0qVKjkr//LlKxO919h19+69XN2677vLLluWU56//urceec516uXc7/95mjTddzUqbXcf/5zoevYcc+c0lSk4gmUlfZTfEnTh7jggtvdRht95a67bgnPQ/qwUd/Zs61PreXuvPNG16HDLlEv2TMkUFHaUIbVVbA8E1D7yTPQJMnZ98Enn/zknn/+Lff997+7hQsXucqVnVuxopLbZpvN3QEH7O3OOKOj23jjDZLELuw0e/Z8vkc/cd9884P7/fdJbtmyFa5evTput91akk5bt+++OwbfJIVjld6V2k/psS0vKRcUtOEbuAo6txKrDeXGLZNYl1zSxe211xB3+unLMwleJMwzz6znvvrqMPfgg08X8VvbDsuWLXODBw9yL774kBs7dpyrX7+yq1nTublzV7k5cyq5XXbZhX71Srf33nuv0T5xbXNR/tkRmDlzpttzz93dbBuQoEz+8Oijzh1ySHbpWOj+/Z377DPnHnssFteuTYZ0wQXOde4cc8vl97bb1ne1a1/KGP/aXKIXibPxxvWyGKkViS4HESgRATW+EuFT5IpOoLQmQBYvXupOPPFfrlmz8YHArnr14kmawP30052bP98xYHWuSxcXTJrccksNPrqOc+eff6yrVasGkyjrFZ9YjiFOOeU6d8wxI8k3N0G7DU5OPLGW+9e/yp6w8a67nnKjRr3hHnggJhhIRGQC+ieecAgRnNt6a+e23dYFAgSboPr+e8eHQV13661XIKzfA3ckC2tIlYWB2+TJ/7hOnS50Q4cuYBIo94rfeqtzW27p3Nlnx9KwiaZrrqnFZNuu7qGHurvq1ZPMEOaenWJCoCy0n5LeiIEDP3d9+96LcK8gq8kPn+8ff9jAoB7P9uNugw1qeWeZGRKoCG0ow6oqWCkQUPspBaiRJL/77nd3ySV3MEE817VuvdA1auTc5ps7t9NOjkULscUcgwatx2Ka6nzfHeRuuOHspO9am/i44YaH3aeffueOPXYxAptlfMPGFu78849zP/7oEAbWdmPG1CRcN8LsHylF6VnVfkqPbXlJWRMgZfNOjRgxwt1007HuzTfn5PRtZrWyceMxx9R1PXu+4XbeeecyU9Evv/zCde/ezbVtO8917Trf7bCDK1THFSuc+/JLGzfWdX//vSVjy2cYuzN4lxKBBAJ33323u+++e93SpUt59zp38snO3X9/QqAML+15MfnQqac65BHOzZjh3IEHOvfDDzGZRYbJFAk2b55NyGzKOOlXxo3hKtwioTJ30ARI5qwUMv8ENAGSf6ZKsQIRKI0JEFuJd+qp17My5Cd38cXZrZZfsMC5U05xCJud+/hj50aOjO0GsQ+t+vXrBDsSKleuzgfZjq59+7ZuwYLF7h9GppUrV0FgvzWD3+0R3m+W0x0aN+4vd+aZl7MynxmYEqiffnJMFGzv3nmHpf5lRI0cOZ7dB90ZvC8osgtn4UIrr3PffedY+eDcoYeu3oETLb4N/h95pIabPr0BKzdu4EN3i6h3qdnLwuD/5psfY+XoGwwCaIglUFOnxj7ahg1bnYh9zPXqVY0VYNuy8+iuUp3gW53rumMrC+2nJLRX0PnttlsXN2DArGDyLNe0+vSpSn95tOvRg21IUlkRKO9tKKvKKnDeCaj95B1pPMEbb+zL6uS3EMwtd02bOte8uXM1ajg3a5Zz9i3GAmZ32mmx70oTvNx/f3X37rsbuVdfvYeFB5vE0xk+fCTfP3fwzTqfb9AVaRc6/PWXY5FLLRbk7MAq1BtYEU3CpajUfkoRbjlJWhMgZfNGXXZZF3f00W9zckDJyvfuu44x49FMIjxVsoTyFLt373vdW289yK6UuW677YpP1FbkX3NNXSaDHkGIfHjxERRinSKw1157spvy96DO9erFFhPUKsFaLFvUdc45zr3/vnNPPunYjRQ7QaSkUC+7bEPkT/3YLd+hpEkhM9AOkBJDVAI5Eyj5FF7OWSuiCKybBF588QNWtIzKevLDaNWu7TimKbaVsX372BbJ1q2dWy/Y9DE/AFpQMN8NGzYcofFwjjmIDW4t3uef18RtPbdy5Qbuwgs7s8LgAAbFmc+BPvPM22zlZQamhMpWHc6ZM9VNmTKj0AC7hMmWKPqNN/biCJyikx8mJLCVGEcf7dygQS7toL9VK+f69FnMxNQ4Vitdzor0Hmz7ZknQOqDee+9zVo+WbPLDMG3BnJF99NmuGluhasqa6OWXL3W33z4W4XRfzItjHvoVAQi8++63rk2bJSWa/DCQXbos5/iWoe7f/z5Lk2xqWSIgAuWawPLlK9hte5379dcf3fXXx3YLJ9tpPH26c08hU7SFHXfcYUK6JW6PPaaxy/cKFrs85DbbrK774IPvmNC4w7388gLXsGHxWGwX59NPFzD5MQJhyf+x+vteJl2S7960BUE//jiBBSaj3fjxE/k+XUGe9ZnUbuZ2371ZqU+eFF8bhRABEciFgD3bX375ec4r2aN5HnSQY1fZp4ydV2U1bo2mkS97v36PuI8+6uXeeGNeMJmcSbr77OOCXTAnnXSRW3/9pzkmcL9MoinMWiQwjYHoiBE/8F4aw/HSSxHY13etWu3iWrRokZcdEL5qK1euDBaq+uvDDouNg/11LqadUrEJ6xdsYaYt3rQdIflQe+45l50kX+RlAiQf5VEaIpArAU2A5EpO8UQgBwI2KL3ttidYycLMRA7KBqqmX3kldnxBsiRMgHzkkTE9ZIhDaOz4TwubCFlE8EW8zOfxQfow6byJkP5Gt9VWmyZLpojbl1+OcP/7H8vx86D22ms550ePLhMTIBMmTGM15BQG24Ur5ldGXoy83XbcZKr259SHF16YB+8eCAzu5YiIrTKNWi7DWZueN28Bx2vkp/i2w912NvkJEJ9q9+5L3RFHDOO4sUMQjmSw5MpHlFmhCbz11vvuuOP4M54SKus3W7de6b799nfOa25ZwtQUXQREQATWDoElS+xdeSU7PsYF54Dbjo9Uqn59xzEuzp10Umxhzfjxjp2cq/i/uVnuootuZ9FMd3fllXexI2RBVpPMtnDhvPOWsSt5EpPKD/PteGWhIth/4PXt+ybHw7zOkVxLmcRe6HbccXmwyGTatEp8n9ZioU5l6rGv+7//OyOYiCmUgC5EQATKNIEpUyYzxquUduFYphWwo3W32KIS/9E4hTQzmIXNNOEsw9kq/X797mHhTeaTHz6LTRlqP/30HBbVdeO0ga84bpU/eiiBms7s9aBBAxHSD3cTJ45ncmhlMHncqtW+7qCDOnIkl75js8VrE2yDB7/DKQ53uiVLpiIXWMYOn/lMeDiOMavGe6w2/1FZGZnAKRw7fhX/fcV2jRKqBQvmM6lXOUilTp3s5A3psrZNGnYE25gxsZ2f6cJm6sfcD4s8v880uMKJQJkloAmQMntrVLCKSGDgwC/cfvvZSoLsa/fii44VJ84NHJj56gBbSbDjjrHjDR54wAR8sf8P6dVrIav6xjBRchmTKf/lBd+g2AJNnjyTyZJig2UUoGXLRe6XX37nI4JlMWtZvffeNwyyi05I2fmb++6b28dI48Y26TQPAcAt7sMPezMA4Ou9gqp58xZyjjjSjjwpm/j4+++iidkH6I03LoBrP/fcc7cXDSCXdZLAiBFj3H/+k5+q77JLARNsozUBkh+cSkUERGAtEDjvvNs4BnWcu/bazDO3b5bXXot9K9rK0U6dVvKtORZh3fW8d+dnNfkRzfWyy5ayI/YzBDGHUqbYjtjffvuD4zlupp+dwcTKEmeCwcLKFtosQABli32GcH75Z5z/f/Ea+0+RwmXR1bpEYNGiRQiURzI++YXJu7ms1t8AQfIOjKN2YncyqySkMibwF2fhbbXVyozDFxdw661XMgHy11qdALn++gvdPffMoV0UV9rk/jaGPvvsWQjSb2Zx4v+SByrG1RjcfPMVbvTob93xxy/gSMJlwf+C2jBz6tTRrND/hD67N/1nfY7c6oUQP2F1XzHpr6vefzPwvOqqs9l1+JO7++55wXGRhVks5XIWR+XaIsc+/L/Gi4w97nOHH86K0xIpGz/HFpdWY6OkyWzyoexUiueeix1zaenmQ9liimXLjIOUCJRvApoAKd/3T6UvZwQGDRrqTjihqLC9uGrYH6A/+KDjSILMJz98mnZkgZ0BaedB2jmq/sPtoINWsXphNiv//sWMft/giAKboHn99SGcDT3BrVixLEhi1aoqbPlsyJ9zLQmOI/LplsS0MhQULGCQMZHV/uPZQVAQ5N+ixdb8MWcTyshbdg2pH36wYxpWFMrN/tDTJps+/LCQc1YXbds6uP3DLpDh/MHygVnFLU+Bq1SpTFvJX4ktrVTzRbaN/N//HuPsz1jr1WOpjNQ6T6CgYDEr6fKDwQbY3347OT+JKZV1gsDff89BYDaWIwzmskqwSrCYoGXLRkn/RHqdAKJKrlUCr776EUeMjnQPPZR9Mey77IknYsdl7bmnHYe1kO/DPxH0xIQz2acYe5f7hQsDBtzDBPMY17Xr9XzPzgsW5KRL047sOu20lZyZP9ede+4DHI35D7tCjk8XRX4ikBMBW9n/0EO3uy++GO523dWEkPPchhuuQNhZ2T3zzIbBMTK7774P7e86vutjE3k5ZbQORcrmiOVMsKxaZYJi02tHjWEp/eLFE5jILVn+Xbosc/vv/ybHCt6GsL1mVom99darHNfcnePA5tAvrywyJrfJ5FatVvF/nbM5/nA2Av2TmGg+iWMQ7+C/QGO7DLLKcB0JPHnyn4zTD+WdN50J+/STdnakeLduy1lAOoNFjhczKTcJ+crFOZOqw7YP23liyowNS7YxKF4OGxfZZE3durH//PKyn3iAHCz2h+p16pCglAiUcwKaACnnN1DFLz8Exo+f6oYP/4GVbNmX+brrYucz2/bIXNT228eOOLBdIJaWV7YjpGvXOe7002/kaKy/2PGwhNUpC/mAcghwYqGWL3esNJkT/Pk6R1XyEeVj527OnWsrE953o0Z9wkBjMS/opRxDVcUNHbo+wiTHcQg78IebXRiIbJt7JhnGnDZtZvDfE9Hgzz7r+L+T1QyiftnYL7xwEf9f8WKFngDZYIP1nd1P+3CzYy9KqsaOdSkHGJb+nnuuCoQoBx64W0mzUnwRKEQg1obz0IgLpaqLikZg8eKlHGUxhIUFr/H/Wwvdzjvb/xYsZWVcZSbOq3Pu8kqO6WvuLr30dP5PoXlFq77qU0YJrGD1QI8efThrfnHO72IToF1+uWOFcuybc5NNbNW1y3kHiKHaYw/7U/SJfO/9gWDuRo63mufsmzRTZWV68cUC/tPkedekyVbusMNIUEoE8kDAnpleve7imXmMHVNz3H//m7gAx4Shs4NFPkOHvs1/N35K+zsDwfKNef0fgDxUpcwlYUdVTZqUhwFjWLNJkyrxH0Ss6FtLauDAAeyIm1Pi3G1s3b79MhYefsR/L3FMQ4bqxRefZjLuRo69mscCsOIjsXHJvf32HBaNPccxhn9z9PWTvBf0fZtIznZ9nXHGUUwsTQ1OfUj0T3Vt/1n58svzWCRwN+2yCfeyY6qgad3tnjRo0ICFfbOD97b9d2s+JissHZtfs11Hdqx0Ph6dH34wedDEtPWRpwiUBwL5ezOVh9qqjCKwFgjYfyTcc88zDN4u4+W2mNnz7Aphuz/m8M1l/y1REmU7QGxXw9KE3YtnnbXcjR37CyvyZlPOhcF/YfjJD8vPjh5qyVGipu186Hyo7793fGyYEGke508vZRWFY8Jjhevde7777LP5HHXwlbvkkmsZkDzIipuEAuejAJE0KleuVGQHg+20Oe64SKAcrU2bOrYhz3UzZ87LMYWyH80+3ho1qk8bKnlZTQBtE2DptgC3bFmAgDEPmZW8uEqhDBCoXbtm0D/moyg2WN8qX+f85aNASqPMEfj661EMks/lXfgE769pnBc9j3dZAQKxZQh5l/CfC/M47seOpfiWxQY38D8Kd7r58xmJSolAKRMYNOhrFggsYTKuZBkdzyYL2/1qApS99469k0uSosnc2rZdwYTgXaywzW7yw+drgpw+fQr4Xrwv2DHs3WWKQNWq9n2dvThjOau7LrigMzuLHmF3/ByOskmc/FjN1nYlH3KI7aKfw+71x1kodhwT3rFd8qtDyRYlsAUSYv5Hmj+QjrrmZrc07GhcS3NtqREjPmHXGoOUPKg2beYjlP4y45R+/vkn/pfiJiaCM5v88Anb+P2uuwpoqx/w3yV9vLPMCIE777yeUyCmZzX54aPbe+nRR+ex8ODyYALDu2drnn56F07BqBE8K7/+mm3s5OE5xS84xmu//WKnhyQPlZ3r0KHWR05w7703OLuICi0CZYxA9l8MZawCKo4IlGUCS5cuY3XAdW7y5Ff5cI79kVa25bVJi86ds41VNLytKLDB7KefFvazHR2XXeYQ5BR2j16ZYNpW8dnLr6TK0vrmm1hZkqVl5TngAMcfxS9gF8r7bEe9moH44mRB8+K2zTZbF5rYsSOYbEeDrTrMh7LVwT/8ULEF9ieffARnotYoMS6bGGvUyDnbYpxK1amzivZQkMpb7usYgd12a8bzlZ9KjxhRiwFus/wkplQqHIHXXhvOCuAbmKj/myMolqRcFW8C33btHMdJLuBIrE8RnF3iZszgpSIlAhCwCbF+/QZzjMbNTAycyY7bzoFp108+OSjnCbMhQ4YhyGHWooTKzgu34ybtO615c5eXxQ316i3kvf0ni1tW5lw6W8F68skL3OOPD8w5DUWsWAQWL27B9/pBQaVsbJGN6tnz/1h5/VkgILaz7TNR9mz07LmQHUyfM7Y7lAU7I+LH12QSf10KY4uj9t23fXB0c0nrPWiQLQJkYLgW1aRJfzpb1JYP1aSJcxMnZibpXsnRC1de2ZUdHHNyOu7VvkfuumsBxxve4+yP6aVWE7A/kh827HW+63KXMWy5pXNnnTWX91Kv1Qlnaevc+VR2fcROU3j//Swjpwj+2WcuWNBq8hR7l8+alSJghs42MWMTwb17L+A/Uv6tfi9DbgpWNgkgapQSAREoLQIXXHC722WXUe622xYzux87IsjOUMxGmXAvX/9h1qZN8tV8Hdm5+dFHq0tlA4kvv4xNjNjOEzvz9JVXnLvvPsf2b+duucWxtXl1+Gxsn3/ugo/I4v4I3lauXHPNUlZlTeSYrh6l9rJt3XoXzv0Pz/uiIrbbZqONsqlR+rBbbbWEIySyvOnpkyxzvied1IFt2dUDQckHHzhnR61ZG7nrrtiuo8kZfHNbm7NjN7p1S1+9+fMr8WeUaWZI0kdfJ33nzi1gu/2P/Mntp/yx7RccRfKns0FVRVCdOh3iBgyoVeKqzGMR6ciRVZgA2b7EaSmBikfgs89+ZqfHQzxDCzgDPrP62WS+nRV9+eXT+LPS7qzCZBmr1DpLYNGiJRxH8rDba68z3bhxvRGgfkF7ms6u19mBadfjx/cJ/C1ctrtff/jhd74384PX0rHdmCbwzcdCd1vccPHFS3M+msvXys7Pf+aZgaX2PejzkVn2CVSpsoDd27VoC5X4nkHKm6Bsh8fbbw9kh/lxTOg157nahkVg2zCe2Y6FVXuzKO0ldu4tzKlNXn/9Cib0fmDX35GuQ4dW7tlnn6ww31QJGEt0ec45V7qHH64Lm9yTsbi9e9dl180VuSeSh5j2Pw32Ts+HMkGy/9+H4tL76KPhbtttZ5aob7f/g7j44nlMguQupC+unOXR/7nnHndduhQEJ12UpPynnrrMvfrqs/RHrKDMQdVm1d8NN9zA4r/a7qWXSv7OnTnT8R8wdmR0bNLCTvC4+uocChZGsWpdc01Mb721c5tvPpejqHmpS4lAOSWQp668nNZexRaBUiTw+usfM+P+Iys3Vh/hZEL9777LLlM7AmvbbbOLkyq0pZPsGCs7T9T+LMu2GdtL07aC92G37LHHxnaGfPWVczZxMWqUY/AZK8+559oHVWzCIFV+ie42kL7pptgZ04l+qa7PPXcZH52/s/UXyXopqI4d92C3SbX4MVi2WibblWTpihVLq+jgLF2c8uY3YcI0VkNvgpDPsTXWOZvc2m23WDv5+WcXHHHWqZNz6Va22ASbTRK2b5++9j/9VIsVs3l6INJnVa59lyxZyirjQQz8z2YFeleO6+nJKqD/8j9Ed3OMyNUcaXcyz++d7pdfJpbreh544K7u559ruj//LFk1+vdfj512hwV/ZF2ylBS7ohGwHYiXXHKH69t3QUZnbyfW/8gjVzIQne7uvfe5RC9dryMEbNJ5v/26MXn/rvvkk/kc77kUgawLVvTaN4cJqOza3M3fwtlRaxYvU7Vggf2fWqah04ez3RZ2fM0//+RnQQj/H+wOPjh9npn42uKUjTZaxkrmir2oJBMWCpOawLvv/j975wEeRdW24UlogYRqw4JiQUCkCKIi4sdv/VQEUfxUiqBSFEVRQKQoWACliIoNKdKLDURUREVQESlSlY4g0kFISCEJKf/9zLCw2WyZLYEkzrmuTXZnzpw5c2bmnLc87/N+RcRADeSdx5E/fwDlvRdQ1yGSnB9ClzkAOGcdTpBUk9Zq5EgjaAO9DOE6bvv2ZCLWd6DXvYC+1PhfibBfRYKBF1/swfVfxxx2qfm59da6Rvfu7Rmf7QAGbgIdjyc1xDJqVHFocW895cnnzzzzdDMfUoiXkeOwnTsN46yzKuXY5uvHpElv4fwJP4L07rsz0XU/s+148dWfwrR9wYIvjTvuwDARZhG1+SWXZBubNm0MuaWHH34YAFY9aMqLEO0TcjPmgYMGGYZsNJqnpBuJDktrsPKbhlIEZlRuWLGIqPz3v/GwiXxv/XD+OiNQAEegaAHss9NlZwTy/Qi4klF++mlyDnSRnAzTphkky7J/CfK8Cy0SiSIHjC8kjhbwceMsB4ciPWTA9lZEDSVKrvvvN6A9spwlH3xgGDVrequdc9uAAZaBu3btnNsD/XrttRTjzjvHgpi8gbGI0GAcO2nZsrHQlVwJivxHrinTNCCEGyrqfj3btsWgiJ3lvqnQfFcUwZAhk4gOmgV6JQkknCVwebvAdesM45VXlDTOMBNNli17otaPPxokorSiRU5szf1NzqQlS6JQXqvk3ulsOT4CS5duMB57bADPdQI5CdIMIXZyliOms/Obb+YTur0MBf4GOGw7YvwveCJBNBL+q692BWU/CARWzvk25zX7/rVli8H7XwaBnknNKc4IeIzAyJEzMZYdDguI0LNnKvPjbBONXKECi61T/jUjsHHjDmSXHsa778bbQvEWKyaQSDrz9z7zuI8/HmJUrRrYWBZJ4IbkRBlPBPLs3Dm8W0WOWVMOFl96JEqtWkeJTtlCUleEUaeENAKKRlu7djvGsX2mQfTcc08HFHEBtLOhG6pD6kiED5Lu1avX4xje5yCXJkBxlfsEUiEUxdenjwXGUrRyixaW/iNHpN0iGqMrrjCgNzSMdeuSMVKv4X29Cb1oLjJXLqHLbrMFpt6aNWtw2HYgr+U+dKdDpsFV4y2H7qFD+3lHt5Jc/lucTsVx6pZm/TyIrIkQH0T5/vsodOazjVmzhgZxVN5UrV37GuhWV5s0veGeYeXKUkbt2tfZauYPrNfYxcMumn9FMbxt2zbjQj28TjF27xZ4LzIDUadOElHkq6CNZHIJsTRrdoexePHPRDwZhpg5FLkRbBHQUDk5Bw82yFdkGJ06GbCQGADiDCKAlJfUYK6y3tNAbQsUO3Cg1Z6OdZXatbNheljo+nn8fwL84UuXLiFP51LmgD3olMV57+tgT6prjoto8ZzijEB+GAHEW6c4I+CMQKRHYO7c3xBY0ggTzNmyEO6ijtoYBEhACS2FxItEUTu+cltooZwxwyBk3Lfzw70PWsdatjTgrLZoizZscN+b87ucOFqAFX3y3HM599n5pYiCq69OJ0/JUjvVvdbZty/eRFP++edujL90yK28+OJjLOZxCEOW0q8xEkInEmXlyiiMHhdHoql81YacHx06DOCezuC+JBk33ujb+aGOS+EU+qRxYyvBvJxMei7k+HjhBcuZFogWTY6SmjWr4aSKy1djkZ868+mn8zFY9cHYtg9Unjfnh9Vb+TruuMOAuizJKFZsDoieJ0Pmnj/V13/LLVeiKFxrPP98TNDRW0qs+cgjcdA09AZ1zQTtFGcE3EZANBXjx8/CUXjUbWvwX0uUEGgglba+DP5g54gCOwLKA9emTV/WOXvOD/cLFQ3ViBHxRtu2L4AKDfz8nXFGmYjJipLVZBhatswAfe3eq+C/i16wVKnIGT5OO+0o0dWJwXfEOQJD9BoATL2QSe+HpvQ5IiRegx5zMECWPlDttiKHzNPkbFhaIFHimqt79OiEPDMbo7l354fnI6B8c0I333uvBeqSsy6YIhlKeSEE7nn7bVHE7DXatWuKkRErYyEu7747FCdtU2PQoA1Q9hwy2QIUNeayb4pVQICoN944jE55gDFKI9dkCZgFivsE4bkPlxyw779fgnE935gyZY5JDeS+/1R8v/nmu4yZM7mwMIuubc6cEkQEXh+wpXg4mcuUyYoYCLJGjTSiAf4IeN5/QwVR5Ekucz2zwVyz9AY5FJQ/tWlTS5f6/vs0HH7TzLxAwbSluomJieS3+i/r7Svcn0yjdWtLTw72Vs2fb6D3iTLOshv973/Km2WY+rlsPAKrilZd28XO4KsITCHWD7GBaHxk5xEww1UUiXnoEEr8sbIVqpInn2yD0+YKcsW2N84+ewgAjonkjh1j7NvXjbxJd9KHOrzL43n/eQGc4ozAKR6Bggf3PMUD5pzeGQE7I/DVV/NAjLLaeBQZHYU2euopAy5+A++4RwUvPxV2KC5mb0gmL9X9blI7as+zyBAtFNOcOQZoHs+9/n/LsP3OOxYdlhKpuy+SOlKUWuKOVNSHEAR2rtnbGZs0SYZC6UejSZNrvO3OtU3K0MKFfxCmPg0U40bj9NNFzRRloh/++isLR1A5o1WrpiiDN7O9DArgMxgqhiDMJxFtYkUqPP10rmaD2iAB48wzKyLAxgZ1XEGoPHDghwiPv6E8ptoWICVItWplJTmX0CgkXqNGFs1aIHSo6NNefjkOpahdQRieU9JH5fkYPNjKUyAF1E7R+9itWzrh+Nsx0PZiXhoO8rfgYSOGDu1K1EsqCPvfGIMUW1RFovbr1i0OhFN3Qrtr2Bkup86/bAQ2b97F2pth0vqFe+l33XWU5+0naDHvD7cp5/gCMgKiPbvllkOG8q+FUoT8vfnmgyZ9Wq9e7fw2Ua/eZVDxbDcd234r2tgpqlbJnEWKFCMPCItvGEVOlKwsFnsDOGkESmpqNLKHmzUmAm0W9iYOHEjAQDXYOHx4A/+TTLq1nIY/y/K/fPk65PlXAVCcjxGtD88AgnMBKTNnfoJj7Fsi2YOPBFVUuwBir75qGRDtXrKclMOGGUbXrlZkc//+oofZgz7Rj6hoGiuEZfjwARhRR6IvJpoG5ECXKEDZtGlJ5PgrjtG4DEC7DOPRRxOYp7LNHEPux6enG9A0RfHslSW65nbk0cEAU/KH/nT11deQw6k0IMZDYUWBCKFfo0Z9dNLA71ZKSgrOHxQntyJUvqiGv/zSMAjCwTlu0TZL91bEgPJ0yjGn/E2eJS7uqHHkSIrn5n/lb+k5mZlY+oMomiPkYJBjQlFjYsKQA1S6LPnUeS8WoBPfwzxUHkDWcGgsUXADlCN4XVu2vA1HxSZsENxMyksvWfdR+rKcIbIZedpW3JvlMTHnLuk048ZZz4bsLerrLbdYNXW8ngk5an/4waIjFytJw4bWc0MOdoMADvOZ+vlnw7joIoN5zPAatapnrgTeI9lZRo9+G2DP68x38SZ1l8bCvdx+u67pIKBSyTF9YYEYyxoz2b2K890ZgZM+AjbMrye9T84JnREo8COgZJQy+nuWSy+1uJ7/8x9rQRP63XOx8DwGdhqTNksCTThFHv1vvvGeZFqLuRwZwYR/u/dFSroWUS22igpRAi4iQUHLWHlFxEcpfutwihSNoUM32Grir7/2Evb5CoLjbtDdSaaDxtOmu2NHIvlMRpMQcRKG9SdAvjUi6eiTOK7eMvr3TzLD48WhGaxDyL2DI0aUQtBnQApZEf3EF198TeSHfeeH+xA0a2YY8+YZUFhYzjH3fb6+DxxYAkPQzYUymsbXNQezPSnpCIaN13imQ8tT0Lp1BsL73zgMZ+JIuDuYU+eLuqLGGzmyN1FG34BCGotj8wjOtqO5DNeaBzU3jRkTSyLi05hbnyc0u1K+uAanE/lvBJYv38SckxaRjlXiMdu+HeigU/4VI6A8TJMmfYWxIbznp0uXNKhjviZhc0uMDl6sWsdG8557bsH5uxDjV2JY46uIDYFlVq0qjRGyCgaLVRhnMkNqU8CFYcNika1kpMuwDZbwd7KNG2MwPjlztr8xct+nHG13390NI1o8edoy/d4DUd+OGZOMrrAe9O8TxuTJg4i6vdC9uXz5PQlL3muv9cIofNjv9fnrfJcuFu2MQFuXXeav5ol9itCXvqMifnx9YmLSMPJ/hDzWC52qrLWzkPxduHAhCO9RXF+iVwO7v8t85pl0HHDJJI9vAS3WUQy23xHNnXXcmSB2hPj4aKNx45uJmHsK2r+q/po76ftE3/PCC2/wHj3E9SeE9JwB9EdPLg8SfrCt/sfFxRIdgNB6rAikKOP4NdcYGMst6mmXf0hzrBwin35qGcOffdaKInB3dCYmFsOhRNiTU0ygV3R0jAmKVCRIoPLVV9bY9+5t4CDOHTkif5YcUK1bH4SC6iBAlzZE2DfBvvAmdh4Pr4DbyV5++Vl0283HnR+uXZpLFIUhKqvrrrMiTQQYVESmu7NCdh051S67LJr3qTjrfxrtZZsOMkVquEqVKlYeV9GgKTpLnx07LJpLUVQr+k32jusJTBL4059/TvlgL7nkcnK6PoPj+FMccspb5jqT9/8CVLz+ehK6/2qTKtB7LWerMwInZwSiTs5pnLM4I1AwRyA9fc4JySOIS6hV634WrvhcRyhpniKjJWhLiJGw8uab/qM7FC2oBenjjw3CCnM1aXuDPP6iIHLncXQdrGTmyksiVH6o5dAha5GuX99KmqlIkwULrATY4To/XH1q0KA8YzbV9dPr//nzV6F4DCB0+jBGA69VcmwUauPpp2NZzK9DUe8KPdkOnBYDEAZ2IWikh5yMbO7cKJAY1QiJfR1BOe+m2qJFo832hcTIyOBhOQmlRYseOJjWhOXUEvJEtFmitfInfOr5Hzq0BOG6VYnOGQgKxvHbe7vFgwdPNI4e/QjlDItTiEXK2U03lSHPyoQCTQclehRRDU2d+hXvxBGjSpUo8xkT7drWrdn8Po9IkfuZ8670q5iEOIz/usNOxRx0sgb5rbc+Q1H+wGjXLjJnbNSoNO/XVGcecxvOwvr8fP75LxgmhmDos9D1bpcc9NfnniuJUaMHAA2sIj6KZICGDR9BptxlAlp8VAu4WYj2zz4rBtq5K+tBfQwlHXGsx5tI14AHe1To168EQJTbcaiso731poHIo0pQPxWt3KBBHM6ZacffocL6/AQ1MD4qJyQkY1DuBJL3gGk09VHN62Y5Atq3L4cz5B10j9O81jmVG4sUScKg3hjHWhFk7dFQ2L5A/o/w3jVFsQtdrzyIdooMiNLnRB+sYslQBo6m4kSh9mf8HrN2FIK/R/Fm3nBDbWPChN0mOjyUSxJy/NZbyxHJ/Y1xKYjAA1AP7DzGN3wuFlI7URGhnDeSx/Ts+Rjy8SycISlBOUHkDG7XrgyI/D7Ggw92tN0lJZafP38/z7Zh0jS//rpFT6gG1Ob8+ZYRW9Ta+q38ikLvy3iu72JocOlY99xzGnru9zidsII7BT22BSDJ76HY9j8Yn31mALAycAj7dwy4tyLd9aWXSvF8X0dE0xSvusZykmy98MI95LiJ9wuG1bwi1pClS5VvyDABBbqnclJoTVy3rgx2jP4AFurBslEUaroexq5d62CzSGZbuhnNKUeJ7B1iIQm39OsXCwVWU3KMzCbPZKKZLyyYNpcsMSNc8s4wEkxnnLr/yhGI/ldetXPRzgjk8QgIZayPZxHPrJKga2Hs189yDiiMsn9/Kz+G5zEKdZVwo9whjyFHe+73bN/Xb4VHKs+CBCjPsn27ARrH4nr03BfMb1HuCLGgfkpAk8FIOR0kkEWiaMwC+RGE1u3a9WUQi/acH+rXWWfJMZSM4vIjUR/vmslG5817DzTZy1BoKTdA8L1fvVr3tKxJIZCXzo/gexb+Ebt3/0PizG1BK9OeZxb3slAtEjyFZmrc2KJRGz/eQKm1aiuR2913xxJOfK3j/PAcQLffSvwppHG7duG9bEL/NG2axhz1nVvrBe+rkkyLZkiOnJ9/nojh7VWMFC/jzHwDB+p0ItOGEyVytVeFpOBdbf7ucWJiCsbKP6Eh3GRGP8hIq8+hQ4kYPxJYH1jk8nGxaBIi10FRLkRHO3pf5EY0/7a0ePFyZKIjEemg2lm8eIXftiRrvPHGs8hAcSHLXUJ2jhkTRcTH3XxuNOlBx4x5EYNdadPw4rcDbjslrylqc/v26lCBtAfd2hwnSkm3GqF9/f57yQ11jjs/Qmvl33NU9+7DydUWH5K8piiIvn0TyCk2yJyz8/OoffLJaJ6x8N810cWI/kVGRTtl0yYhoU/UlAwllHaZMunoVXNO7CgE377++iucj0khOz80BMWLiy4sHtoclESKHB614UfWpyA4P9TnAQNGgJy/zqRPVV4FO0XUSfffXwZjdHvbzo9/CC1az4RcufJFZrRHuXJWrkTlZpIjSU466VCKCpHDQzRsffpYLAyKENAcLBpmARv1PKuvO3ZEGeeff76dLv8r6rRo0RHdCS+RnyLHksZ6+nT7zg81F42FtV+/FOaChdgCrOfd8zQjRw4yevb07/zQMZpXxK6hfuh+yy4kh4YcMrIpXXNNFE6tC6E4r0UkyGWstV/iqPyRd2oIjpO7iTq6GntGfXSf4uaz49mPYH4LxPvll0XRr75G3gje+aFzXXVVMGd06jojEPkRKBr5Jp0WnRFwRqBixXIgjeMNCcdKOLVtmyWAiAtVwsvQoQaLnsXNKGH5888N6A0MKFmsJOVyHCiiQih50UuJl1aRGyNGWEm3ghlhCT7ijxSPpLugrja075FHDKN7d//8knbPp0VN9DIuNMWFF1rXpAiWcIvGpnLlM302k5qaDqr7ZZJ8JZnUSj4retkhQWXo0CPQYP1AREIjIm5qkcCrNnzakzAC9ALBsRVFMNUMO/Vy+PFNclB9/HEREJhlEZZezTP+5P3748mt8hcokKNGhQpxCD0XnTTE/k8//Q4KLD2gM+r4oPj5IiosCfJ6riWcS1hXpJIo4mJiioFeOhPB7XEiRer6acXZtXbtdt7tTBPtFe5oNG+eRsj1D8wLTcJtKl8cX6pUDMo12qFTTtoIyEk6ZsznoMi/B42WjnIexf9suLMzWBfT+B7Fs1qCTxEoLwzmrliQ5g0w1DVHOfc9x5+0C3A70UUXnY2iqdh+m5YOt2M9vwrQkJVV1HG8eQ5MIf39xx+bzGSjkbg8UWtMn44lJkC56qpqUGDcbjzxxGzAGym8awEOcNstNLuSoz72WGvAMq2O76lfvyoI+0Gg2V8E2R7Pe3rU8JezS06UZ5+NY969FqqXJ+lDESJXGhKVO5I54MhxypvjJ7D5Re/PsGFx0I9gCXJKwBFYs2YrVCyrACQxcCGW22/P5rnbisFtFZFAdUJsJW8PEwBkH1mJRakabilSxCAywTB1t2rVArcmRLboed2L6IAV3b9hQ+D31f24/P592rR3MLAnht3N2283mAvmon9mntS1UMmXN4OqWr16Fc/Lbs5d1DQa16lzhVGxYkXb1yWE/fvvTwX5/hbRLG/hIEwgCXyW1zlRevzkycUxspdBh3yduflOv+dZhQL94YfDjV9/XUgEUbYJ0Nu1Kxm5yAIxCgS4YYMFNJSzTg5hgck8i1gXOnWynmNFKEnXuusuzcP3mYwBnvX/rb9vuOFGnsU4bC8JXiMcpdPLsaSoSDmggi26Xy+/nMxz8i6gq3uQhU/oIskovWvWLDMdpsG261m/XbsEnpt3ABI2Pr7r3HPPw+7zoPlxbezf/xkSmk+G5QLFO8QyZkwJ7A5n8NxvCYsiPMTTO4c5IxCREQhCNI7I+ZxGnBEo9CMgLv6MjGyQ6xatlCihRC8lwVooEIWkCkkgwXngQEvYViItfYTYmDjRosVStIhyUMhpolKzpmG0bSuOVMN47jnvCc6smif+SviS80OOj44dT2zXNymT2rd7t3XunHtD+yWOSYWQu8oVV1i0Ww895NoS+v9ly6JIJFrLZwNvv/0xwmW8be5ez4Z0f958MwnD71CQDeNNIVHG05kzh7L9I+gnPgNhfwSHSEaOXCmKrlHEhwTRb76JQQm7xPjuu75mknXPc4Tze+/eQ6CmZhLd8i3nz4DiQgJ3NknLonGGRIGIjKV/zcl7cAuOmphwTuX32JUrf8fhAgQkAgXQl0kFp6bEHyrHmT5yyL39djZjfxQn0rEXIALnK6xNWHkKjkTk8qQnnoMoAABAAElEQVT8b9iwMyJtOY38u0ZABo3hw6dhLP3MePjhFGP27Mwcc6VGQwqlELbvv59irndy7JcsmQhKbZZx773fQeFzLQrpY3k6hwVzV+rWrcI6zeIQgWLl2qoUgZacJvLbCCiqadmyjaC+15AgdS0O/SMY2/72ahQLpe9yOKSlHbV1aK9eDxF1kY6s8i2yS7Itw7A4xHv0KIah51mMeY1yneeKKy7hvR0NGGc8RpbvMIZngOJMIX+SJYvCZAPNVTTo1Fje8dOIgO1sAklcDYm6ctiw7iY96aefJgflmHG18cYbxWmzsePQdg1IgP/vvz8dQ1di2GCVxx5Lxuk0Od86QPagWJ1zDiimCBWlnhDgKpADRAAy5TlUbgD3Io7+Tz4RwMze++p+bH79rvlt8+YtYVPY6fqKFTOItI/GybSRMa6e55es/DAffvguSPmxODwymD+SoXRLNeWPn38uzRxZjPnoTPTtZ4mWuMuWg0CRoZ06dcWofTcG5RGAtj6j7Sj0snRklwz09GIYt4uhnxXDqfwQenFHDOi+LeiiAuvZsxPAx+U4oONNg7uc19Lhb7vNSlwtY7po6QRaVA4K6deBinRyPZ/Kw6mE1nPnehgCAjVQyPcrN8fAgSOxhbTiXU7ItS79+qvFEhFOxIKicZ55JgGH2TDuwzvHR/T339dwD6PCnp/VoHI3deuG1zVA6d79JeO///0aEOMe09EboHqu3XK+TZ5cHuDiAaNJE4R5pzgjUEBHgOnVKc4IOCMQqRH45Zc/8IoPIFQxAQOQRV3l3rbyYgi1oeRkP/1kOTSUyEy0URJukKlMJ8ddd1mhrnKCaLtC0WUgFuejwh1nzYoCGZ/NQuY9ibrQ9BLA33vPQi/IueJe/vzTcq4oB4OELJ03EkWCrRwrriKhQZEtojQKNcG6q62pU+O4HgbPS5HhbcKE2QiZoaMa1KzAGVWqpJC7ZDVKPtZ5igSkZ555gHt6K0L0LATk7xGcj3AvMkl+mIaSk8X9iTIqV442kRxbt25HSe/Apw7PwgNhK+tSPMaOnY2QPgHB9wjXmNMBY3aSPzt3HibvwRgoN6ZjhOyRZ8rqISRyf8nRXP2x81/OQEU5eRY9k127ZnAP9qE89OBZHoqwFgF4n+eJCsnvPXv24iiKjLKtsUd1LyQj41zGyRqBI0fSSEzc06hUaQt5D3xHy2k9U9SjPlLMmzeXM0QJPTOZYxPhWZ6HMWEliTyH5ItokIoVKzDXlyc31OGQFEb38Z82rSTz2R3um5zv+WwEkpNTATT8CVjloLn2X3hhRYxa5yMneXeCaX2eOvVbqCAm8ewf4blO5j3IMlG5zz9vYADzn+PN7uUL+FK6NJYUG0VUWK+88ijOiLo4QYYTXXUE2pVUU77R++cqMuDKGfnBB6VwrpxLctLn/b5zJUuWQO7sSHRIOwxpvwHiWQ0VxmaMIRlQZZXFmFOb5MV1kYcucJ0ix39Fcv70038xyn+NLJdiGkJzVPDzY/z4ovT1PJyqjgHPzzDl2DV//gqegxybQvohUMpTT2076Yh9u53NQOkQtVKkimQgO9S9ysuosfG0a8vgqYTCaFaR6tIpb0dyv5gJ3OePcDpVvfoRgDYb8twB8ssvC9G32zMnH2SuSieqwrPXVkTLpk0H0ZueAoD4tqlrKR+JnVKp0vnQHA0ByDWU6La/cFCsJcn7Eebq0rwzl9uKLFm3bi26XXMM2P8QYZeZY4xFcdSqlfWMKQ9Ehw4WG4RsAnaL9HtRY0lXHTXqdSIS3rB76L+iXkNCtho1aosd5kMcS4k51iXlTW3XLvxhuOOObBwtX2MfyUCWYIKhbIRbq3p1jCMRKLINRUensxanMxfmngy1/ReSwqxcuRhA7CWAjQ4gF2Rgp7Bo1PRuBypyCj/0UHnW7+cAib6Yy1kU6HhnvzMC+WkErLcwP/XI6YszAgV0BL7/fjkIuoEY4gNzpEqI1MIzb57BYiJeTvGKnhAuZRjWdn3kPPjuO9EExaBgV4NL/TnCdxNAyE9B6FpOou4oFM4jIA0zjf37o0AgZmIMt6JOlJjPlThdkQorV8p7HwvVUCmTduTss7eZSdmlXHsK8aHcBiXYcl9Itc4/8IDliBHlV6hFydxiYs4283N4a2PFis2MQWQogP73vySMb3OOO0Bc55MhTIp/8+Y3mlQQ1aodQGDKok+qISQE1gTzk4qgaWAE/AkaihVG/fqifggN0ayQ7UcfHYRA/RvGDHGJ6lzei+T13r3TGO80HC8vIzS35lisixEuxfByiQM0EkXOMgnnvoqoBd56Kx6n4AtQk32AYOosWd7GSgYvRY9FqmDTc4ozArZHQEbgBx7og/N1I3PPUdvHiQpDCMUHH9S6ZDmgdXytWnuhangGI+vbEY+ks905t4qdO7fCKPKGSSnktjmor4q0nD8/BmRnw6COcyrn/QgIQDFjxs8436aRb+oAMpXkpgxzHR83rhg87FlEO1xmPP54K9Zzc8E3O7Vz5wGcds9jLN0JsCUdY5eFGle0rgAryokmKpxgjFW+rlbt1KlTw9dur9v/+9+rkGPGAyBYgBFwBrm79uMojzajUg4ezAalrKjaqkQU3w+HeHUMb27eEa8tWhtjYooDBGlgfvxU87qrX7/2RJEUBT36Je9UUkCUvRxIffuWAgl9IQjdV5ADcxt3vJ7oX75RNKmi0BEoKdyix0KI/fXr/yYCoHK4zUX8+PJYtffvj5zQomgmdz3GW4elY5FSLFf0h+pKZ5PuU6kSQnkhKWkI/TEx9uYHO5csAJnazMsyY8Z01uye0E8lGKJj9lckh8j4/e23K3CW/B8O2o9YBy4/bqz2d6z2ae68kJNUrlwZI7d9Yfzvv7fj/GhGdP8Br9E1Sr4tMKOKkleLTSHU9aRPn2xYEmbiuNY6Vt9q1PlrjkDz5q1xZK1gHV+EkyjjOF24bCbKaRpukU/issuicfqt5z4jYFD0/MtuE6lSvHgUssjRHA6QFAw/I0a8Ch3tFOSRDNb7w9gtlIfOYiRZtsyKDJLMIhsNj2+uIn1w6tSi6OLlaWsKNqilyOgszE5xRqAAj4BjTSrAN8/pev4Zge3b94FYH4ThPMnM8WG3Z6I1GDvWwBsvfk4ryVX16lbkyMGDBguNnCSlQROVxUHyKIvzlWbTp59eDmGtF97+o9AtbOPzF4vpURavUlBpnY2nfzWUTD/T5m4WuiyTdqQEEIHatS/GMH4nnv+6IBwPgV5qgyCUZebtUN6FcIvyf3iGisqJo0gVGbu0yAZbFCHQs2dpqJ98e1CWLdsA+pCwlwgUhZIOHUqcp5cyb95yokEGIRAl5rpO9+oEjZjXfPPNSaCb50Gf9QcUL8PhzSztXi3g9+eeG4HSsRSjWWoOVJC/AyXoi2KiZctJnK8siOMb/FUPel/VqpcixC3AsGJfyPd1EiWXEz2bv3Ilj/wNN/zDmE/hOcBS6pRcI1C5ciWcm0CAjPAVytRUKe8RsJrk6qWzobCOwOjRX4B43BSU88M1FjI8iF/5yScV2Wg5RJXYs2vXg2wbjOI10FX1lP2/++5GrNOfYhzZyFwevKFNCuQzz8Syhj+Bcuq8W6fsRno58YYNf5u5w6pWPYBhPuUYoMG94hHTubxgwRIcBWuJAqqHAeE+DGvTMH7+hAyVbdJPCLSiyFtRmP72m5W77ZprLGrM++5zby+07/PmxdFPhJMgixwGrVvfbH4EqNi16x9TVixXLg4jb5kgWwu/ugyFoui68cYGRpcur0HPlYTzNNHMdecyOgs9L4fPrFkleeeK8e60IaK6iW0HTfi9LPgt7Np10Bb1md0rPffcDOhyD+ZLB0gZkEGpqUXRh+xRAwe6ZlHa9uvnuxbpRnifrNyMFSrkrqdnV7pd9eqNcu8soFsU0ZCQEPza5+ty4+OL42Bg0syjsmTJYkCCz+HYTgiYG0/rsyLhpkxRDsts5pn9RFrcgBxcBv2rNLredYCwnsCAHZwDOtClCTjy1FNtkQu8Oz80DyoSSc/Y/v0G4EbrmQvUrq/90ktfeikBQ3Y/1inCb53C/V7FmDzF3LEdp/xhHMaZJguHbBUCb8pRoOiKSJTLL082I4RcDpCyCA1bt8bQNEpXBEpKSjbPq9qzym8IIk8//SAOvQPk1kw3QRmufa7/TZsaRv/+Amxa16sII7GX6J3YutWADaMooN7SPPvXIu+MwKlenra+JF+KZVcSm4icRAK/aqyUq0bAxXPOcZ3B+e+MQP4cAccBkj/vi9OrAjYCnTsPZBENzvnhukQJJeJBv+kmA0eGtRAp1HXDhiLkEbkT3s5bcRxg1fZSZEy54ooq5sd9d716l6JctnDflOu7KB5q1IhhcUwxeWzDdYAIga48GDJkuRcJD2+/bVFuKZz04ovd9/r/LoPCgw/GIiS2AynsG021fft2Ft3IICkkbCYk5HamiBZDzo/p0xNBUfrvt2uv7u3jjx9FEd1NaHMPE9FsN4rhhx9Wwin+I/fGvvPDdV6F4I8dmwza531Ce2uDZj3NtSvs/0LCDhkSC/8tD2mYZfFii7s0UDNPPpmGweRLhLkHHAOil8GqV68Kc0hkHCBS/n3NN15O7Wz6l4+AKINef30C+Y8IMQyxCCkvvnUhHZWEWeXuu7OY+zagmP2G06GetfEU/ZXRduTIvjjxn8RofMg0eNvtihTJ55+PgR/8ahDzDe0e5tQ7CSOwYMEq5JVXcMAlEgHh+4RS7P/v/wyc/kkgdX+Ck/1HnCHZOEwsPnvPIyULKbpXvOtCk8t45crl5lnXzm8ZXNetiyFquJad6j7riLf+vPPO8Ln/ZO5QsvZffhnL5w/ALV8zh6wjIiXJBHooyrRWrYvIndfYeO21RhignKiPYO9NdHRko0KzsiLDVR/sdchpp/lXH3+lQYPriJT/zARa+asXaN/ff1uR+HJoeivScfr2tXLXNW7srYaBgVDJp+NwojzkvQJbRUmzfv16aHT/MaMMLkYxOptw/UDX6bPBPN4RR6bt9PTiOE8jYxBevbo4EepYmfOgHMFz0L37w+hA8QGdH9K5n37aMty2a2dF9VgGbxZuI8FITEwgcnMa93wuuT2qA44bbYvWys5lzZo1E11yCwAv77VlgHbpy8ob2rKl/4h5763k3KrAj50712Ow3kuC9bNy7vwX/ZLzadiwF2FWGM89jceGcuLiFWWjd3jCBHtUeCeO9P8tJiYVAMLu45Vq4mX5/PNYfofvABGLR2xsGZO2UydYuPBnZJQHeQcOBaRulXyjPLUwgZnOn+HDS9BWaZwZlQDI/h/Osod5Tt2NHkWQ9w0o4yyGETk8tFuyrpx0svfou3LVKBeu2neKMwL5bQQcB0h+uyNOfwrcCCj6AJEiJGSo62LFSyrHgaiwxPfZp08ZeJl7IRi5rcquyhH6P2HCTOicUkzEooTAcGmwpBgINeFC8bl3swbAGYVXNmlihfHec88Jui/3eu7fZYh9+uk4FtHWRrt2d7jvyvVdSlIkF1lPXevo0QwM/q+wsNt3frh3slmzTJAmexC0Jpk0Wu77vH2XcNar1xsY3ZJDvi45cnr0SMQx9wHh3L28nSakbVdeeSnJEEtg1EkMy6gjAUm0Nx9+GLgbohNp1OgoxtDlOHWuDnzAv6zGRRedjdOulJkHJoecGsI4TJ8ei7OOcC2nOCNgYwSmTJkLXVWaX3o+G83gKLZyYbkcIJqDn3kmGSV10il3gKj/Mhx/8skQHDQ9oUBMQFHMMI21/q5Nhuvu3WMxNFzJ2tHNX1Vn30kegbVr/zKdHwI0nHeevZPrmezQIctEOSqhrAxS3opkEQFaBCqRMUV50BQZEmrp2lU85M3yrXE01OuSsbdhw8vNT6htOMd5H4Hzzz+TnAQIWREqW7cWMSpXrhih1nw3s3XrHsAcM0HkL0PGtJISy6EoZHO9etWYd5t7pWtr166r8cILP+CcPBRwXvZ9dgOZ24rId68jGjblbJSsqndbuT98zRn4M+DHF91NHZMSyb0d8f9/9dWXxrhxwzBA7yBCJAoZOhOUfxQOkyJsiyLKvwlzxpO5jnVv51R9v/LKq6Gi/Zz1OLweKLH3P/8UAyWeNzDxKVPG8RwcNOk1/fVUER+636IyczeAux9D4Itx5536HCT/0CJ05utMwEeDBqA2wixjxgwxne++mlH0h6iTVJSMW3k8wi1awxo1Sgdctwxd6o5wmyuQx1v69RMwa8zCAZKUiyZQ77jyoyoSWf8jVUSNN23aG+Tlus90olWpcinvvRU5Kh03nPLtt1pL/89sYif8fN27t0VePeRznvJ2LnycADwV3VYCOXcgz/q9uaotXbqU+W+cyVgycKDhMx+oIkPeesuSe/T/ggtyNeVscEbglI6A45c7pcPvnLwwjMDo0Z9gpA8fDd+CgA1FSLz99iWE/r+Vp84PjbvQj/LUK9yxd+/whCtFaiixe7NmOZOga8GfP9/AeRAH3/OZ0EH1BzlQFyRsaTgpo0yuXPdnQOgi5ftQ/e7dz0E4fQ3ah7vcq3j9LuTUzp1IdhEoir4RXZh7mTBhjtGgwUEztNN9ezDfe/ZMhc5lNohQtKkA5ddf1xlnnplohpYqkWrbthZPvsZYQvuePQEaOLa7WbNsFMnlXiNa7LWQu5aMFp06tQD9UTL3ziC2fP21pURWqmTvoAYNUozFi1faq/wvqXXokJ6RLebn/vvv4H2OCevKxWv9668lcFT6gUOHdQbn4MI2AjNnfksUIRN3mEXUfVLOFUrvKjJKbN++AxRm6NElrrYi8f/SS89j3fyAJNkNMLCUhmowyqQ9cm9bjl0hN195JYZ1rixG8i6mA7qIwgGdki9GICMjkzXsZQxf9p0f7h0XWlK0nnKC+CvKvyDHBwGqrP3+avrep/VeMtFXX/3mu5KzxxkBjxEoWzYWWqhi5I/z2BHCT+W027o1yxDQIq+KZJlHHnkJAFgXHIyzQC7vQN5LBMmcCKo4EQrZ/bxzPwEi6kc0VieoZP7K0RWhqStUuAK9IvR5lvzVpnNj2jQrubkQ0UpyLhSzHCD9+/t3fqhDos5KTy9F5NIHOfqnaI8mTa5l/ehCMuQ1vNOHcPQcJIosASN4PDrfP+xT/qEJ6GQ3cp0vmnz+ORo5xT/atn2KCLjyYfdi0qTiGIEf9uvQlZFan1DK5MnvMYb+UfWKNtWcPHu2b+eH57mbNMkiWu0fgGWtjBUrVnjuDuq3In9SUvb4RecLxCaHmsrmzYbfulYte39r1UqGbWKpvcqFsNbkyWPRw79g/c/t/HC/XJcZQFRkkSjr1hmwWcRDFfuA+WxLJmza9H84KkKfs9QvvSZjx5aDLeMJs5vduz9ivPhicM4P1/Upd9Hbbx82Bg/uxRgdcG02/3/99WzsPP8DuPiP6Yw7/fQcu3P8uOgiy7Eo8Mf99xvM1zl2Oz+cETjlI8Cj7hRnBJwRCGcEFi9eayYnC6cNHSsEQI0asTgKeuY50urw4WQMTf+YxnTlWNAC+uWXlgNG3LbBFClHnTuL4uF8hMOyRBz8TXuZZpulSpWELuRSELNNjMaNa5sC7x13XAOdw3aE/0/JY7ISITCF0OIoQqsl8BYz85S0b3/P8fp2+lKvXnXaA75ghO+IUuRJnToX5zjtqFGfgNryL1DnOMDLDwlTrVqlgiKbbQrQXqqYm/bti8f5MxThI5lxshKpukKfFSEkvk0JFDIYKhTfFSLtrT3Z3G6+OQMn1CqcU+EjllznaN9eeWTm4FzZZqJkXNvt/pdc9dJLBvy8do+wEv/NmrXR/gFh1NT78cUXv4KSWmFs3LiNpIaZRDaVBSVWi+uun4tyLoxTBX2oEJIffPAJ7+tC3ptM5goLlahnY8uWdEKsLWekoq6CKZoDlKdg4MAnoWQITyAP5rxO3YI7AjJQbNu21+8cFMzVyeGhPFK33HLiKIXXy8nXqFHNExtP4TcZFj/4oA/zwg6SZX5KUshl5IhIxfgWzTyRbezbl03y2zOYo5uASL6BdzQ8p+QpvNRCe+pp0+ZBsXQAeofQL/GppwyTukQAEn9AZjn1Pv/copcAAG7mDtC2QEXzsSg4+ve3kirrHVD+jnPOOS3Qoc5+ZwTMEWjSpBEy1hfInYRQhFG++86Afq2OX4N1GM0z5/9ptGnThyjAwxglMzlP7taETlZU1U03JZLEORFjXzf0jucxpN9wvPJrr40EqX8t0Rf7ia44vtnWF0UldO5cDufLWcYTT2zGMJlpSLeRDO2tP94a1Xv+ww9FiBAYbdJZuerMm/cta0FHri3e75wj+v777ssCXX3IdKDcf/+POFC/YA0p5WrqlP6/ggU6KupS4+uvlwIACO2Zknw6eXIZkPcdc1yLjK1TpoxFV/kCJoRdyKBZZu6lYsVicArVAmTRAcfXDUTgROc4zvOHqJ1KlkwBYe+558Tvv/6yElvPmWOQu+zEdjvflONA1Frt27fmGpaGfG9WrVqJbswD5qcoykiUbFoL9AzqWYxEIWWOkZTEA1/IiiIfPv54AvPDfBy22xi3LMasKPSqlxI1diu5VlsR9XEEu8NA7l2iLXaFevVEJ2XNPeEMl3IrKufl+PGG8fvvW3jWxzMvtyOitCu6+XScowe9smfYOef06UWROa9CDr/YdMxlZW2E0oqHJsQix8YjjyRgq3gHW0U/s5XVGEZefbULDuZ4n1Ef3k6nKNjRo9WeZWOSU88pzgjkhxEomh864fTBGYGCOgKpqekIJulmwrtIXIMEotWrt5KIs1IkmvPZxrPPvomRPduQ80NFwtUHAJZkWJciIDqSAHKmeZxCw2UIkGLftOl1JuekuSPAn+rVzwfh9LRZS8ZljWPx4kX5AJkModSvfymIziiEGyv5YAhNHD9kxoxS8AjfePz37t3/EBGSElQo6fGDPb40b54B7+0Cnw6QWbMWgqx4kzE9jLCWW+CtWtUKyRWqYv58KypEgoWMML5K7dpHSMq6JiwHiO7PihWbjeXLN/J8HCRkuLhp4OvefSLJYBPMBKa+zu+5XYgmPWcy7BC4Y7tIB1Q/8rKIg7x///fJZbMYgTQdpT8NRcfieReP+8qVq1BkZxgHD5YGYfMEynjdvOxOjrbT0tJxtI4lj8x3REUl08fsXPOO3kMpdd27W8nlBwwwbFETScHq2zcGAfoawuKvyXFe54czAr5GIC3tKM+gkob6qhHc9sqVLRpI96MuvjjF+PPP3fnGAeLqm6JBhgxh8aNoXhKCWY7D008vy3hEaEBcJ3P+R3QEPvhgOutWalhtKrpD9FaK8BC9p79SrpyV30ZrupKNvvyyf0oIGegkVwnsoChalcOHlattIUbaptYG52+hHAFFCE+Y8BXy3a/Me3uQw7NNQ3D58nEYz2vgKGhqG4DRsePd0Fl+jyyZhGwd2nCJfurtt+Mw6t8XWgMBjhIYqk2b5zAqHzbzQAWobu6W43LWrCSi6wYwPnFcY0tz++lY7uSUfvjh5kRV/GMot5SdIrqWDh3KGt26vc460xjql//DeLoDR4R/A7V72599ZqD/RGMkfB8H/m3HdylS4MUXOxEtGO/XKH/8AL4ILPXCCykYDv+gXy1gBvgy36wpw4aNZbwbAdY7aFIBuvc70HdF+D/6aBlk7DfJzVHWrJ7GxsGD+8EMMB2DcApOonSzXdcSKp1u5co9jMFi5s3S5FX6gHW2KOCkXwCILUHnSzbi4sqYSZqvuaYh0fG7AdEd9dsVUUnZlY29NSQ9rEWLfwA/DGLuZzIPoShv5YUXJvk9UmMgR54o1SSj6120o5f7bZSdSZy2ZMkgPT+BGj2F+xVN07fvE+RNXcwzlMi7k2HSn8lhpGdu3bo9pk53221DcTKcy/wQODeM63LatLFyXcj5Gk6Rc1RRo+pTr16JUEwNY/5qC9PDmehrA7C59ESWUBL24M6yYYMBaLYCkUzvmgdOnDgCZ8Oh4BrxUvv++zPQbyfRtxfMSLSuXR9EZgrO+eFqVmA82SwkJ40a5drq/HdG4NSOgOMAObXj75y9gI+AEsDGxUXO2FGmzFGEkwjErPsZ102bdhpr1izPRd8gJIy4bV98Uc4MixarQQPv6CcJFRL4RQGhBHJJSVFEsFgCrZ9Te90lo1FcXEmv++xuVJLMZs3+A7/mVxgl7Cstnu3LyL1wYXEUPi78WFm+fDMRIViWI1CEFN2z5xCCrHKW5EQyTZ36LciU91HskgPm1pBgrKSsc+davPlyKvTo4b2DQiz99NNu7zsDbN258wCK70SM6r+YzjKFTleqlIVAZCBsluSeR4NoLWI89picZpmmcOevSSEJRemlj4TBYIocc2XKxAZzSFB1f/xxNci/gTgX5ATJQMnKebjuXe3a2dCRJWKYSERZHgC68ioUtm4IrR6Vcx4a9q+DBxNRxrtxz/egKKb7FJLVZ+XZEbWvkEa3326YeVb88a8qGqdHj1gMt8pTgJT6LylHjqRB97UORXqtqTTLaH0BA3XlldUxNFU1jdn/kqEI+TIVAeIyVITciNuBakuKvnspUiQbJG7oc7p7W3n1PSamOM5cB5mfV+MbyXY1lx49mmBGUIbb7l13GRgyAjtAdB4ZskSnU62ajK2GIadIo0YGkSgG8o9llFq+3EJJikJN4BIZvFxFQJEffljpOEBcA1LI/ms96t9/JOv7j8YDD6QSIZsB5dQJg+eBA4dZr3YhO/2KUe8M1upeAYFSF1xwFhERt0JXOhvjE0J7CGXs2KIYlxX1ekkIR/s/RLn1Hn64P9di3/nhalEo9ilTDiNH9mXNbmiu3dp3+eU1MZbPJb9Pa/q9nf+SWV1H5fyvvIfjxhWHguY0AFljiGa+jvUnGz3oe+TZltyLddyTRHI45TzO/ZfoaCV7r11bFtl9DveEF/xYSQX2/fTTbaH+PWTb+eE6Vv/bt08DEPc7htExRLy0d9+VZ99dydm3bdtq6iky0GpMy2jAKcrbMXz4JJ7RVhgzDzHG9roi+b1DhzIg05/kg2BK2UU4SJs2TZBTd+PwS/Uq15ZENZQe2qBBAjRQCeh3d6BnFOe4dHSPbJM5QU7i1au/wjlSFuR/NPpass9Oybms3FzSn8Ipujc33DCV+/s8zsXgvYt6zuTcDFTkZJfRWDK8e1L0QMf52//HH6XI13kM/eivYgHYp2Tf3bs/xOcQVKO5o8fkTFQUcZ06aYAK0tBnD5mRlUo87489wXXpigARjeCPPyoKzrU1uP+i1X7zTQOGDOs4zScXXpgMoHA54MF6OBTvR6dcx1w4DifDYRO0prVflMSKHNGrx2uYS9aWvPD446cxv3xMBHIFs/GlSxcRxR9c/7zVlj2oUiVFeG9j3VlIfpEDZo5Xb3XtbHNRvCvfrBwiGhOnOCNwKkcgb602p/LKnHM7I3ASRkCGj9TUwEKM3a6kpBQBXZ4E8ucrnBRrSEyeyKIXbVxyySVG48ZXmMkiw0WXij6nQ4fkXIup+iiU/WuvGYSQGggTFpWOokSkvCs8WwqDKKJ+/92idJDiLoTE44/HgeypYvcy86Rejx4PknT0J4TreL+UFP5O/uyzsaBHOuUwfioC5Nxzw0OLup+zXLkoHEZHchjzRQEwbNhI0/lxTM9wP8Tnd9GmjRljRVR89ZVl8PasLEEqFCPlxIlzQGaNQcBPIiol2wuC8Ih5Kj0PTzxRBARfERS2KFAjJ9A3MmZaeSUsOg/1118CSc++u/9evToKJbym+6aIfZ83bzlK7EAcaEm2UG0yTHz6aTIOwF+IZjlI4riBOZ6ZiHWMhtLTj4JI7M7Y7oSLOtNW07rf7dpZxrZ77rGMap5KvCizJk4swTMXA2/1oyaVRGZm5OYyWx09BZWEsB08eJxJIdawYRZOrWQUGwtlu21bNApILJQcRUBn/dfo2vUBkHJoUE4xcxdNnPg1ztQlJG7cxYhYFBUJCcmm0yKUOcZzWJUrwRO1u3dvCQww5T2rOr/zeATkdJKTPlx5I4+76bd5ReUoeigzMwtQQTmjcuWziGTcxDvv5lnw24L/nbI5KP+ZAAF2kJtyTIvWUsAFcWJrze7UyYqglY9PhjxPB6B7D7Zs0XvnlMI2AnpG//e/nqzv8RiC03OBL3S9oiURuKFJk0QMUokYoJ8GVdseI/B//Q5Hr17tiEj4DWPf36DW7ckPrgaVWHfKlNNBT3dxbYro/7ff/hiZ/R+ogEJrVrLygAGHiLzoSD6Hb443chEC2uef/4RcNgWD+XCM5AmcIw09KtlE0O/YUYKogjjmhiKM+0MAfLoQkYDF71g57bTTaG8OBsvJ7H/NNFY2ahSPszLbdFbKeCe5d86cItCOxgKa6Q7g5Ilcc+X48SPRR/bbdhK4zu/+/5VXEpGpB3HvWoZMt+Tenq/vixf/it45hOtaznobjfMtFUdDNk7XEsj/AridyVg+A9DsbuiEGiAnzQD41Jrn8QBzWKpppPXWtqKSZ82KQuYqx/P6CtFILc1qory6//5biOrYhW5rT+5EDTbmzcvmvGnoyZZD2XXOa6/NRAc9aOZb6tbNMCl3lNxchlz3MnOmwbvjviW079JlGjbMADD3s0nNFWwrylu5cCGNGEl+DxWF0PDhBmuXlYfGjtHeb4PsXLgwBv2gXqBq+X7/okW/EE3RBgdmvC2GBgHElEdTzg9RfSvXjz9wmAZAcq2eIz0zenbkiAimaD3XOcUkIEozV7n22sOAr341HSDa1qPHizhjz+aZepm5PsV0ushxKwegnIdy2slxIL3u8ssVlVcKWfxsaKom43StajYreq+oqDTzGHNDmH9q1kw17VATJrxJPlbfTkU7p9E4duxo0F9Ft4kOnMnbQOBxijMCp2gEmA6c4oyAMwKhjkBsbAwhuEVMCigtruGWRYsyMI5PAcGditCbhVBuoQ/WrFkCEmmKsX59NOjkyzGWPx1yQsJ585aClvAvcCrEXB8LWSO0v4VQRC8AUWU5RAR6EVe7FubffhM6HqvwKSzly5fGefM0gu9gFJ9knwK5ry6+9VZxnDyXIaD/X44q0dFROZCYOXaG8MNySCANHCsyND366CsgRJKC7rOakOFFyeybN7ecUsciy13Ng+CQkHf+8d92vrzyyhjCrr+E6zfFRKr6O0YI1h9/zMSYHgWKriR5QS40/vprP4dkgVTMQOFMJaIoi33hJfGbNy+OqIsr/HUlpH3iVX/qqVcRopNwdNlvQgE8QlX277+ehMdj+I90lwdlwIAPMdDvse38cO/CNbBZSRm88caiINRLIVhH4VCRU8oyCLZseSfOzltREkuZyEfMb+6HF7rvX3zxi9G795ugqJPMZ1bosJwlC+dwojnvjR37KcrIdwj+z5sRITnr/Xt+KcrxhRfew/AgZ98Rxi+ng/POOw3ed0tBD3dURPmjaDL3smpVMZTDU+tcd+9PYf2u3EJjxsxAqf7N2L8/gXUl25RrSqKBX3llNSLfmmP4qp7LyJffxuPvv/eDmP0MA+gCDAEZGD0NDMrZRHlFYxzLAs1dAWNwasScdjJMCxhyxhmBR0JIVBldVISclnHk/fcN05hnbfX/V6hhp5z6Edi2bQ+JuddhHFqLAywJ43AsRqnLQKlfhrG8YlAd3LFjP7JbN5DJB80IITsHa12fPTsJI9Io831s1epWn4eJVnbmzKHIDz3goN+F7J/mBcyS83A54955pxiG69P5DDPlg5w1wv8luXfcuFkAEdLCakxI/sGDN+HM+BOd6IQOogTDLVu2MT+iG1q5cgXr1GZk+UwMkedjuK9NUumqppPXWwfk+L3vvtY4QFphqFyKPPwzxrsl3O9E7ndp3t+rMWw2ZG6s73VO1Ls6efIHRMqnemve9jY5eZo0SSXa+GPud1vbx9mtmECoWZ8+Txi7d/+MgyIemccTMGUBnTZvPoQs1A2q5KHoKhPQ92rjAFrK/DUcPfBDM0q+Xr0E5ltFtyu6Q/m8yuDQKwKo4QYAQy+Z0SPql8amW7dHAFftse38cF2P9B2B8xR9R8COGR3i2qf/ingX0l75k0Q5KMCVuxNEuctFERuJoutdseLXkBwgtWrVRm9DiQ5QZDhW9IAQ9HK6teUR0LZQixD4pUpV4h3Aul6ASzyLbrdu7dD1LWo5rcGywQiUGagI1Cl99NFHNY/mppr2PF5Okv79LYChnCDujgzPuu6/BYx47jnLiaFIHvdSo0YmOufS45u+//5b5JbhzNOWk0M5Pt2L7AaiQnvrrSieuSLMBY8AoHgeOY0X4lhJBo1RpgwvX4RK+fLpyIJ70eP/Cegocp1SIorAdcrlJ2YK3RM57WrWVD5SA7CdgQO/PKDJF3HEYUxyijMCp2gEeDSd4oyAMwLhjECtWhchIC/HOBBOKxaKUAr6r7+m5EITStCTYWj37iwW4tUIXI8Q+t0IpelphPEY2ydWmH12dhrH2DtEwrfOrY+3ggxsRiA0bnwlQpX9fkgAzgtk6W23Xc2i2wEhYhR0UskoON56nXObQkwHDIghvPgShKnnc+7kl5IiLlkCDMNIzrUv2A0SDoQec6f8mjNnGciOBBSKYFs7UV+5NJQoffJkKyH9iT0GwlIpHGpARmwWRX7I+TF+fO7n0F8TbdpkM1ZHQHQl4xCbiJJdzAyhr1u3NQbngyaC0d/x/vZJmdq2rSQGgur+qoW0r0uXVzHwJgbl/HA/Ue/eaSSEnMszdxMIwRMKuHudUL9v374PA8FceONDNxIo38rMmTFGly494MU9l7mlKNd6Gug+yGgpRYtGTmAO9TpPxnHjxn2JAWMsY5Hsl9JCfdG817XrUe7rARwifYj06Y0Tqe7J6Ga+OseGDX+DfOuF4yMBQ8ZRnpmc3ZMi3qyZoqEsR1vOvcH9ktIkvm9RzbnK7t2aL2PYhufdKXkyAoqSEPf9+vW/gy5M4h3JzoFyTIIw/Ndf92MwWMHaWgHDaG/Wq8p50pdwGhVF1LBhk3EwzAJteYQoi8xcco7W30WLEo8jOl9/PefzFsr5ZZiQwc9OURSejAKuoigQ0WvYLeeee7rdqk69PBiBefNWsBaM5p7tRyZOZb1PN42r4tT//fe5GIdLIPecjoH1EYzCWNkCFFGhPvLIi9Bd2Xd+uJrUGjV5cjKy3WjkopoYnt0mTlelY//LlYuDzmkEAKEJAKtEE5sC7UpWLsCNnkWh9UeNiiUy43rQ/53yLAJy8eL1OI0yAgJsPC7F689WreJxNFh89d4qnI9VXJ9QivSU+vWvMj/BHL9582bWrTRbjtFA7bZokQKtzdSIO0D2wN+lKIz27XeDiM/wa1iXI3nIkCQAM5uofzt0Ux9C4fcf5KReGDN7mE6iVat+wynyu+lkOvPM85GZrgGhfzXPGQ+rW/n227mcayUyM562EIpsvnIKPPKIks7nnn8ll7gcBQLsCXXuKqKR0rV4K8oFI4O4gBiu5OPSraSbibJXyHv3Iv3yl1/WuG+y/V0RIKmpZTAWHwxoUK9c2YoCkQ3gk08sx47tE7lV1PrXr19Z7tcLblsL5tdnn+2Ew/MAtFEWOFPAP0UbSb/W/VXku8A5Amp6K3L0id5KDAqKTAhUBPYU1Z2cap07WxEhMu77KnK0qb6M/ooA8XRaKbIjNTXFdAa+9trzxuLFE3HcHfb5LEjGkI3pmmuyeeYyWGMmMe83ZD4/4fwuAaLryBFuskeRjCIKL9GWQyxiyhzqjz56hhURIwebp11IjCQ7dmzEeZG7TY9TANy1nI5TplhgEL0zipbRPfnlF9khDDMvS2pqNJFmM0wHaufOvJxOcUbgFI2An9f3FPXIOa0zAgVsBB56qAXG4o0sTKwAYRTRIdx4o38qBQljI0dKSMuCC3gBaJ0/jC++GA5X4xm2ziz+69NOYyWNUNECN2FCCeO779r4bFHOjmXLNoL2+gzheR0IqhTTkJaZGYVyUBZFsh6CbPOgkXO+TihKgIsuqoSQPgjhIBEBKc2rUCHHx+efR+EoiUMJaIpw3xJjsIeFj5PUrVsFhFRkxkzC9wUXnIHggeRxrIwZMx0DVLLrZ8j/W7WyokAknLmKcrX88EMREHK1XJv8/lfOjyFDxoIsCc754Wr0P//Jxlj2DxERY1HqO5noum7dHsTBYDmkXPWC/d+nTyzRFg/nGLdg2/BWXwk44+O3YegOLOB5O17bJGD36ZNEmPp4FK0XfVULafuoUTN4N47kcogG05getS5dkojQmYHBZGAwhxaauj///DvGqbEoAEqYaf+yFFkuWrQWLQYxV4yI2Bxlvwenrqbodu69twcG73io53z34777LCVPaDpPBcr3Ubn3CNUpo4V7GTu2BM8/kDin5MkIrFy5BW75vhhEDmNQys2frZPqfVEC0JtuSmIdT+IedScK9BGOuy1P+hRKo4pSuvfenuTX+AtncapJ1+mtHc2FoljTZ84cw0Rb6rnz93x7a8d9m3IolS/vvsX3dxkD3J2IX35pcXz7PuLEHkWiNm5c58SGQvJNeSDWrt0OYnQLeU+STCBN9ernYyC5mPnEPqgmL4cjJSUVdPzrRBn8ZgI8REXiWe64Ix0ZJZ1rScTIOAhjVh3eqR5+r2Hy5G8xzO8y5X7P9uz8ll351VcTeR8HIbON8HuIgA99+z6M86MpRr+ZROUuwFCdimwsmjsh9rOgiisOZVMD6J/uCTnC3G8n3HYuXboWp0L4cq+arF8/m9yFP7u1bv+r9BPRP/3wwxcYvhcR0X7AlFsrV65MVEMjxuleZPYL7Dd4rOaqVSuZVyJzfZJDNm3aEnQf/B0gupxWrW5HNt/B82df/hUzwEcfHTKpw0aP/pI5tzp6U1H03wbmx985Xfvef38gOsJh18+Q/svILYfEvHnW+uStEdEzK+/grFlWXkvV0RzsabjeuFEyvEVnKAP3U08ZyHqWgVjADBlv+/e39r/8skHUj3U2zeWZmTQYQpEO2LZtF+TS59GVUgK2oHxR775rGeuVSyoUf96oUcXIRXN9SBErATvoo8LatX8AOprC/L4Ip9JOM+qxQoWyOI+v5LlrjvH9RvN983F4rs0CZPTu3YV39VvuU7aZ79B9/ZWxf/16gzxAVpSHcprKeSED/aJF1r0UsEZznhgt3nlHdEz2ZNeKFa2ITdlg5EARpaWeBT2LeqaUD0jOMznRRCc+bJjlPMt1EWxQxErp0uWJ6HiV3DYTcGwl5nouvR2nbZJfZszQO9iJ9WUKUVBsoJQm1CkpKZpn8oScoWvu1cuiQxaN14ABJyKi9C5orPR+yAnSrp1FyekCdCxZksa6MBnZ74jZvq8/P/1kRbrI6aRk7xpXzyLn2+LFeo+yAaC8xNo41rOK89sZgZM6AkwBTnFG4OSMAMmeih0+fPh5Fv7enBHRwXhx48aN/QOdnfwXNeCiRiQx8KUbZ/NJoI0toKemQc8wZvXq1ZGRMr10JD19TkDJUCiuhg3bs9jtYlH30oiNTTLGC2EyerRvdIpnM99+ay1mWVni6H0PJby0ZxXz9549BxHgNhPKGM/imAqn73iU/8gM2UsvGXDD/x/5H3p6PbcQxJ07D8SIcgBhLxEB2UJXuyqLPuv776NwjsSBRBC11zMk8/J+Ha5j7P5XtMvUqd+h7H0CR3eSGYJZoUIG4ZzRLPpFoQHKBiHSCP7aewMaNm+5pTOCw59mLhS75/dWb9CgEpyrPc4ZoCkUKV81avwPwUC5XrwdEdw2CXqi2DiWD41xLULY/y04NfT6BC7dug2nP3MRrAI+9j4bE8VS48aljQULxprPpK5ROSyaNVuPkQrJLMgyYUJR2qpJdM6gII8MXP3ZZ99EiZqDISz069VZJHQ3alQa+q/xCKFIvhEqdeu2wrDxT1BGe2+nlvB5zTWlUUKmIWRr6j1RFAEiZUz3KSODCylkRcarhg0f4V34x1bSQ2+Xv2CBEIcXg6J92xwrb3UK0zah6f/v/x7FOPJ3LooJb9cphVDoSeWPCqWIQlFKmkAALuOE0PGPP34mlEyjMWjjZXRKREdAzt/77uvO2h1cAmIhLFu1imUN60TUIQtOBEo4c5DkL60vN9ywCWfZ0aB6s2mTgSPHipx0Y8+x3YaMY0LlfvGFvUNEYTFunGXIEr2oHC/i97ZTJBctWPB6wMTXdtrKD3VE/TRixFSMRT9BJxSFfHaEaICjIFijyZMSS4JYjU91EOZt+F/Fb5fDeX78NsxOOdeaNXsGJK9k2aO25DStt6NGFeW5OM8EKLlH/LrOp/W2QYOHAE/tCTn61NXWXXeVZn0aEnRkltbGXbsOmmv/2WdXQM4o6Woyz/936TII4MmCkBMLu3dQBr/rrjsLpDHWvCDKvHnfYYjvhoMjEUfHIfN9lJFT7W3ZolyI0Rj7yxK9XI0IjPeCcoSMGDEMQ+ArpnE1iC75rHrNNeW4vj8jJn/06yddayrvF8pnCEVr9nPPXcKavYg1G+uvzbJ//36iSq9G77M58flpV8bd8eMto7SvapqjBayQI0Q6lqizRBnsojFSBIDaGDzYQtj7akfbJZN07Sr9xpJXvvlG+TQexIHxJmMQvBx9FI6k2267Br33TzNRt79zu/YJyf/MM1YkSOXKrq2B/0+aVAwA1MU8z9/yngeBAgrctNca69evwyHckXHZBcDwIE5KiyZV94BHwHQUfP55WWwTJak3CF2cGxOgiOauXbumvFN7iNpQjjL/Byj/pJxZsLyZHxn5lW/Ide+3bTPQ2UTJZDnINK4ewUo5TiA64aZNDaLjrEgT2WDk8BCwUXOG6DBFc6nnI5A88cYbxVlbuuDAGwPVdLzPSJUcHfD4ofO2bl3RpKJz3dOHHroTQMvPZnTLq69aeWPeeiuwXUm5zOTck0NE74PATHK0PP+8Ya7Foq7yVmRv0Ds0apQ9GlytjePHF+VzHvroNp4GpzgjcGpGwP6qdWr655y1kIzApZdeWi0xMXEShrZ6wVwSyZ0eQVEA92AU59gUvjPlG2fyvwG/G6Smpnai7f/gSAGHd2qKEoW+914fFuSeeNKTQjJWauHRwiokgd2i0EqF/x448A9cqsNQrPsfP1TG//Hjv8a4MQMkwhHQdJmEI6aZyTq3bs0wURgSRMIta9cWQ/i722sz06Z9i0FspBk27YseTBEkDzyQjYCUiOF+CYJFByJKXg6o8Ho9ocdGJS9++OE7zM/hw8nwp27F2JCIoFGMiItKIGjOtK1MdO7cEqTG69znFI+z2P8pxMfMmSWgl+LGHSvKP3HOORYCz7UtnP/i2fz9d8NUKiX8ffCBjCYP22pSz8ycOb+AhEJCCaMoIuK++1KJOJgDMvFec4zHjXsRtExX8k/sxmjmHWnseUoJSuPGFQUZcy5GEqSwPCiLFq3CyBre9apbEsSFQpSj8frrQ/SCelyfDC/R0ekhzSceTZmKX9Wqcvz9jcOnsufuQv178uS5OJeTQnZ+aHCk8Iwfvxdj/JqI3d/8POjvvz+D53m/LeeHrqNDB4saYOJEwxDqMpgiqgnRVMj577KjiCbo8cfjQDz2cZwfwQymzbpC3T/8cH8MQcE5P9S8+NQnTUo2br99FE7VmnmOFA90SSNHzgTVujVo54farVLFQmnq+RMKUvN4MEXKvyjg7JalSy0DierLoBAM/ZXklapVK9k9Vb6tJ8P/2LGzMdhP4L1PQj7J9ojYkRM+0QQVLFiwBGPOWozb/wHh3+mUzAWdOg0gB8PfOPzsO9ckW3fsmIGsuYP37CXAGwNzyZpr1/6FwSwlbOeHbnSbNkkYN7/CSN85qPsu2lp/1FlBNRZkZc1BxYoFeZCP6lZUlX05Tobn557rDDjoW9b1BK9oelG41K6dxX0/BP3jIoyu/0ck7YvItvYWOAtU4qPDp3jzTpSDBQtm4IQIzfmh7os+qF693dBffsSYtLR9RcrFUr9+pu36/irKgSyaIX9Fxm4xJ8hQrfoCKSo/gbbLMCxUuhwZoiQKVJS3SSANURr1BPNXvnwJ2rsu0GE+9yt/w5tvjgeE19SMqrGTg/D6662oBVEeP/KIhdq3nn/vp1GEYu/eZQA/Xs48NO2kOD9Gjx7BfPQ6jp148znx7JnyZcl+cfPNCcbevQno412h+p3OWIwzROPkrewmbOPBB+8gemCP6UzxVsdzm8ZT+WC0vpcrZ+DszFlDBn6NowCoom669VZF1Fnyfs6a1q8GDQTYtPZLDlK+Dn1CKfPmxZJr8FNksNCcHzqnopRatz7IuA1Abx9kdqNlyy7MaWvQ5RMAeVpyjUuu9tdPOTx07aJY+9//rKTtGh/JSNOnez9y/nyLXk7UWhoPO0VrY7t2GdAQb4+Yc9jOeZ06zgh4jkCQ4r7n4c5vZwQCjkBUlSpVulBrOR85P74OeMSxChx3M8rSB/yUo64XDpQzcHRU53MGwiVTNFoSuST5jOBzSoqQsitWbAIptB50UBVQiMVMvlElGlu3zgrPVYiuvOpCCHgWGXmHDLH4RrVIB1t695ZgpwSfa0AyYPmmLF68DoWxPcL9hwghezEeHyZ6IRkkRAYe/QyTgkFGp3CL+r51awmvBtVPPpmH8ep9zp1kRn0EOpcWxQceyGThjget0wcj/rZAhwS1v0yZWMbkclAmDRByruRenZVLIfXXYLNmDeHurgS9Bh0NsfTuXQphvV0OSoTExCN+ESfBnkroFSE5hCpt2zYW3vZuPiODPNuW8V7hvL44Uz3r+/t9221HQbYsPF5FHNRfffUmSlcdhJ9YQ8gbfwX6ZATTWHKJXIlg/EZEoyrczyuHmLdwXfc6dr9ffnky/Kp/2q0esJ4QspUqRW6Jrlz5KEmA9wY8b2Gr8OGHM3jm0sK+rPbtk6AfxOJZyIsQ9Uoi/fTT9o0jMhyPG2clHB040MDRaW+QFDovZUsJKatXt44RyrJFiziQZ0+j6Fa115BTK6gReOedT3Dk/WOiIYM68FhlrTMDByaChh0cyuERO0bAhnfemWr0738k5Dal5ItiRnlsgimK4FCCXT2/dorkJdUX0EUoXiVTFUWlnVK+fBxghmfsVM3XdeT86NlzBPlZPsRAkwjwxdP5caL7mlOU4PrLL5MwTn4LcrsbDiP7c9KJlkL/NnPmzxgO14bkXNNZZejJzt6IUWl+rk6IFvbqq0N/bt0bvPrqbFC6lvzvvj0/fz8duPTeCIkjkndLl46zdblKvt6x433Q737JffHu/PBsqHFjFNevDxFR3heAllTSwOW88y5EPyoVuKKNGkqmDDlyUDqLv2YnTHgPx5x9yh1fbXXqlIy+O9zXbq/b//xzE/Mtk2cESkyMlTtTc6u/IuS/okVU9F0URZqD5841cMbac35YR1sgDckrcix8/HER9MpGrl0h/a9R43Izn8q995Y3IxHsNCI9rWnTEujYZYhuLkf0SgnAOVZkhdYl6VeiIurcuSwO+opQTQ0EtDCbd8SmldpOJ3zUeeedoQAzh5pRDXKSBSrKizVxYgJRgPOh52sOSDO3o1nrRteubZEJ99p2frjOq3VEji7JlbLFeCt6jpRLROuzwKh6PryVJk0sZ5ly1YVTZBtKTS2PEyDBpHELp622bdMBb3yMLGEJE6IU+/HHaDNh+ggsY3acH+7nVy4QgTrE7iFbghwgGzbktl/pOZP9ScClUB6r227Lcj+t890ZgZM+AkwNTnFGIO9GACfGHTgrWH6MaP53xXlxh52zNW7cuKjrOBa/Fzju1V27dqUcOzZrw4YNH7P/aX7P4bOTz0l9lhMTU3AqfEi4Yyu85n0I6XwX4/oKqBiOsphbCaW6d5fyZmA0t5AHQg+0b2+FW+o6FJorvlF56RVCGOxCpTYUySqB7qqrZAiYDMf9fFBKz2Oo2w8iIM1EvkieUB/k3e/UyUJCCLH7AXK8hIJQiwSuunWrmREV7m0ocfNLL70HoiLZFmmA2gAAQABJREFUUGKyYIoScr333mEUzv4Y0XILQsG0Fcm6PGuMaV8zgdzq1cG3rHDX9PRqOCVuy3FwqVIlEDICSO85jvD/QwKLnFvNmsWBUnoS7tP6/g9g7969h3hufiZaZxzPYqIp1IwbZxAxI4qugId7raBIps2bd+fYV7ZsLM/EANCKTyOYn8ezXwbBtKiJqBEKSyHqb7xRFERNGYyv5xuPPtodo2p/nvGSOdqJ5I+oqBAv0Esn4uKyuZdJXvaEtska+8j1T70I9X6GdgWn/qiEhGQcz0kRQdkqim3lSjxzhbwsWrQWOpqjOE6Du1ApQUKPyZktJJ2USV8GXqExlatIzn8h77Q2ah186aUSJu3Ve+8NQMFvGFwHnNq2RkAGwA8//Bw5wVKYbR3kpZKQqAkJO+Cm58adoqIcCvfckxaSAu7e5ccftwxh7tsCfRc1hOiv/FFmuLcho4ocLdu2GRh6LEoO9/2+vpcuXRKD7R3Imhf7qlJgtr/55nTe8/k4WFNsj5vk4u7d04ni22ZGU8gYdjKKzjNw4Cjk5mRzTgv1nIMGJdPGWNbenP3esGETtK+RkXGFdN6582CoXTwlx11xxeUg8bE8RqBoPalV6wpbLclAW7HiEigXjwR1X/WeT5lyGJDWIGTjwEpAnTpX4JSKzPVJT6tWDeUoQuW7777AoegFkRdk+0KgZ2QcBHB1yPaRR4+mh6Tr+jpBkSKB5VrlCnHdMq1bigBRBIeosHwEHPg6nbldRnXld0gH6VFEHQizKJn8hx/OIcqtKob+0j71cuVsEN3ibbeVwxncEqfHehzJK9HzX0OHa4bD9VLWwwsB2tXhGjsCJPkQY/gfOOlbRcx55u9Sf/11Ec6Dt7mWw7YialxtSWZ86qlU5sNVRDOAoPEoX3zxOfLoBtaAnHOoRzWfP3W/3nzTonPSGPoqigpSxIioXL3ZRuQoUb4Q5dEItYgu+bnnygLCrEbklP33xtf5lGtEwNZFxzx8AjClpyeZz3YoNiWdR/n8RAWo3Ddy9Ek+l37uXuT4UPRMpUruW53vzggUnBFAtHSKMwJ5NwIYjvWMreX/AzgtAkuNx7pCuONNfK3GZx+Kw9Bjm3P8oz3cBoY+J7XMn7+Kxfo1IhYOgyDJMBU5UXYoLFaJtuQ5V4IsLeruRfrPwoXW4rpjRxSLVLaJfFUoaDhFidMVlrlixXqMERtJjpVsGq+EwlWCL9E0qD9axGRsEsJf8qqEQCnxQjOo740b2++FrkXJxV55pXmug555Zij7ksyQ01w7bWxQ6PlNNx1CYJmOINfaxhEnp4oSzU+fPhjU4nMYjxIQXgJTOQkl0a9fScb7UqJx+ucSQs8773QcFtmmYdrzeQnlquRIWLq0Iii1/l4jc9zbXL36TxT8D+A63gLS8ijCcqoZDSGjpSKWxIkro6ScdkqeJiHSblHdqCjvCI+77roO5es6448/toF4Wgta6A+E+RQiY2JRYi/HKVIDZa+S3VOFVS872+MlDaO1xMQoriEujBZyHnrOORXg5w5N4M/ZkvXr77+L4gg43duuQrttzZqtvAeRGUM90wrdV06lihUrFNoxW7JkLchkFrIQitYW5fJQYvQPP7Sc7nFxRaBBKMkclwkiNh00WSZrZjTO8yic99kYQ2NYi4qCiCuJ4/se1s//nhKqmxAut0Aesnjxet6JjJDXZ/eLbtkyiTXxGzO5svv2k/X988/nIkMh6IRZxNctw4S4yfWOByoynB3E3ixueTtFRgQZTSSDaS0VJ7mdUqZMKQxajXgn2tmpnq/riH5x4sRPMdilBCVLuC5KFFSLFm3AQDWP+QWhN4/LkiXrMe6kHOeLD/V0MiRdeukRM0K7UaOax5uRITgS0bZq0JIdI7POHe9gHn+5/vqavBPFyDOVmktXCvbUs2eXRn9pFvAw5RD47LORUB4lB6zrrYJoYt58Mx79ry2gst94jn0LxUqcHh8fZzqm7FAbeTufa9tHH8VBw/ag62dY/2W0T01NDBqc5uukoldehTLZuHFjX1VybD/ttIrItQgKRvjztvRQGbT93Abz3ALi/fJLERNg1axZmklpquMqV87RtaB+CCDyxBOKXhhFTo7eQR3rrTJU3jg3FgKanA0IbRhr0Q5y+kQRJXCUa4wCUFYM+SmLyM0buI5niJi9zGyGPKjQILU1P97aPVnbMhjQnj07AOZMCMmppH727p2Ck2McAL6WRB5UOd710aMH43BCmQ6jyFCv5PGK+rn9dt8NKX/mG29YuUPmzDmRTNx1RGtMEtdeG23KrpJfgy1vvRUDHe/NMHRs9EoPFmx7ql+v3mHewSXmO/jJJx8R8XPUK62f3bb1PvXpY9mqpk4VnaNACLLLWOMhNhMBnEQd5xRnBArqCMg47RRnBPJyBJZi3Lxyx44dmNntF5wedx+rPWvz5s1p9o/M25qffPIDCvc7OBKS8OBb51LyV/GeK4GW+CB9GbK1/brrLGfE9OnZJoLk4ovD7684TYVuuOCCVJCF2abzQyGLQifKuSLniLecZ0I0KyJEiUDlABEH9qBB9hAxMm5lZhYF/ZJTAdi6dQ/C7VbOG7xg4D4Sjz+eBop4NpEAD4AWCh9h4952ON+rVz8fg/1IUDpDCIdfh2CQSJQFwenFcrYqg8fUqcV4TmJANLekXjOvypIUqMqVzzI2bkw00aE5Wwnul5wtyclxOBbG+EUlCSEyaNA47vdXGFaSzERnns+snpsuXQxj924LpS1hR860YBS5QM6FGjUqH0va6UcaDW4Igq5dvnwZqM0iQ4P1xx+xcNReFHQffB0g2ra0tGKmg9IOP7GvdrRdiuK6dVmM97FJy1/lQrRPFGflyyOtR6hUqBCFUSO5UDtAABawjoU3f8ugLCoBKVE33FCW9bEX60UWnPdlMSqeAbruLxyvuzA6Z6G8laNeFRR9LApOyfMRWLp0LTQSoRkAPTunvEcvvbTac/NJ+S1E/fbt+4/LYeGeVMlLhST3B0hRAvgXXrAcJUJAeq6b3vogsEnz5lqbSzKX10aeWYvhOwUADBY4H6VYsSI4CWMBIXTAsHUT54mco97HKfN888svvwcwJjEoZLBnp157LRnj2BjAGo39yjiex4Xye8GC33gWQnMEe57vppuSQGIvM9wdIKfBvSk5MRJFHPbFihUsdf6MM8pBe3OpMX/+bybVWajjoNx6CxfGEGVzW8AmRo0ahuwe75FzJvdhomD9+msLJCYQkIpkX4GzbuM01aopL8gPrG2+HXF6Z9u1e5KI9n4AxUKfb/WMfPddDGC1wA4eq6f+/8YzYKefHjmd6txzjxj79u31f1K3vbVq1cQJGseW8COWxKJgRydRrqV69a5l3r0BUN1MZOHf0X/ClwsfeCCD3IYTI+IA0RApmqRp02bmh/ymyElrmSP2m9s7dLjYqIzHJr+uBXPnfgOFcqJJmeR2u4P6qmicrl0TAMUMY+173zx2Lzx56el7cSIH1ZTXygIfiPnCnwNEB4q6qxrwW0U9KJrZVQR0bd26LPe7B8DG93Ak7qYtkBM2igAWcn789FMN6MhGMOfVDstJ4X7Kiy5S/sl15qYpU0aYbB/u+0P5rkgp5ddRkviuXa0xkQ1AVO0bNxpmTkW70a+hnN85xhmBvB6BnNbLvD6b0/6/bgSgrtoZrPPj2CDV138W++X6RzL0piAkJvL5mc8C0AHvVqtW7dpjdU/Kv2XLNrAovwPNxwnnh5RbOT/kgLjnHnsKsXTZ++834O+0jg0ietjrdSq5lxRzIZ21yCp0s107i1ddaFxvzg/3hgS0EP+1wj8ffDAwf7uSwE2ebJiG/z17GAC38vHH34FwDF3YdzWlhbVu3QwUm99dm/LN/woVShNh8ZLx+uuvkdPiPyByyqEol+Y5KMu1lyXapjT39yyE7QdQksYQTtrcq/PDdUHt2t0D1VNJ18+Q/3/0UVHuXxO/hgEZHDt2HAD92iwUvCSvkUruHVDywNdft5wh4jrfvt19r+/vUkpLly7lu0I+2XPttbW5R+Ebl/TeLV0aRZLFSyJ6Zbfeeg2osPD7p+S7yclHCe2ejkKVENE+5ufGSpQohhMpcmKOoqPUZmEuoh4sLoBmBIoUWtHMyfDXuHFtMypNVHgNGlxmGnYffPAWDEpXOc6PCIy13SY2b96CMYEJKwJFVId//mnf+BWBUx5vIiVF1FdRPF/HN4X1RQhR0VTJmOxZZIQcMaIYBs9SGEVKQN8YFdCIqjYU8XvDDUUxdjRBLpuG4aM/+c1GQ4HxgHHOOacRcVmGSJw4rqMkiOxY8/fZZ1fA8PA/KFs+QKa4Od8avDzHyN9v0WyK8uk///FXK/A+0kZgZEsDxftb4Mph1li58neoAMNzBLu6IJDSihU5Zdk6dapzjyMjIynn4OWXV3adrsD879v3UQyJcSbII9ROK8HzE0/0xrFY1G8Tov77/vuvMVj7vqfSnaTHPfWUQTSvhfCXrqOP0P7apn1btiTg2HjV7/m0s2XLtnDxn20sWxawqtcKAq706FEGZP1A5A4W0wgUGdCzsnyPQbCnyMrSHGx/Eq5atRoR5lEmW0Kw5/KsL4rpxo09t+b+rfejZs0GgAK7wojwPfexjGnAzV0zuC2KLImNTUemZoGIcImBb6lu3bro2beC6L+JhNcXBjXOEe5OwOamTn2biEhQeGGW22/PRiebS9SLBRBYuXIFc/7RMFu1Dtc8rGfBThFN+LhxVk29h59/Hs3cUQHq1jeMDh0eJwLie6Jd6gAULG0CBf21qXmlefOy6M9NzFwsurcqQbw2/prHviCQmyXTbd/+N45lv9Vt7VSbV1xhAWR1gMBMsv3IViQ6OQFGnOKMQEEeAf8SQ0G+MqfvBXkEJE1V1wWA8juIw+NL/t/mfkEIXNdjzH2MfaNwsjzGvvDhHO4n8PielpaO8DQQFHxOaqfnnrOMw0pKFmypX98SquVlVwIu5W8Qn6OSwSoM024RhZVkY32EdBG/tBK71ahhtwUrhFgRLOK+FLWVN45LUWqJs13J46ZP1zmicwlkS5euJFQyMsYVoVSXLVuH0lzb/oWcxJq1a1+EEwsPE0VI83374k3nQ6VKp3Mv7FsQRQf16qtjCHE+YsigFEqRw2Hs2FKE5Tfze/jgwRPp43IcecHRDohmTcqfHGQKCz4mv/k8lwzuV17Jg5zPi5Lude78E0ovHsQwyg8/CCFTI+JOn44dW6BE/0j/krhvoXdQial37kzjOZuGIW8mtGa3Gf37P1jgUKPBjkC1auczZ4UxcB4n/OuvLFBbZ3hsLVw/K1QoZ9L7ROKqhJgrV46Jwyn5ZgRkWPCMWAy1c5biHTljWjD9UARIJEuRIlEYKKpB87AXB+BRnHLRRC0pV1sWIJI4og5uBQ15O0laMzB6vMZ6+ycGnyScG1bUrasvAqOIjnvcuNIYds8AaNCXdf0c127TydG3bytow1qZMsOaNX8i+6Uh+5UwjdiFkV5vwYLVgETSI2LwadIkGTnnJ9NxenxQ8+BLfHxkIkPVNcnzCQlJx3sp+s9Fi1ZhUEo9nvhYSHYZ6IROVs6CYMo335TgOWwYzCH5oq6oTtu0uQdE9UfoVkdMQ14wHZs0qRiy92WmoyHQcaIHrlo12qtzX1OJAGmKdJaOI93Ms+geyugnXU3ybadOv+EEGQbA6ZlcepDrWDll3nprEsfcjhPlIEZs1x57/4cMKYku0RDK2Bb2DrBRS5FH+/ZFRkfT6bZsKYWOVtnvmY+gpCqyNBkltxRK7h13tMARMQY51DJy+z3Yx07ZxydNMqB89lHBbfPCheUAp11nbvmbJImVK2PdjVCpUSOLiJJ1RAWfGaEWC2YzGzduwskUft8lm+g93bx5E1EY1aH92kxUSXj6matXspEIrKb3PZDzQdciKuihQ4vh/IhjTr6aZ20497mi2dxZZG8X4HPatIk8W0OJbBag8DB2l0wzylE6+erVxWGMiEV3O5c57hXAkv9xdYUorPLQ6e6jveObQv6iSKiKFS80DkMDUbp0VtDzqK8TK2eZIkBUZJuS3Uc2AI2f8tc6xRmBgjwCRQty552+F84RINojDuWWpcos/fmrJQL8jfE1To+dOD+q83me33fz6UD9fQhXffke8VK0qCUoTZo0HxRrQg7P+vLlJCjZZ/Gdh3piRYIoAbqEbwnHyiEihIKcGlpgxDcZyBmi+nKa7NplJa3SAhWM88O97+J5bNLEEvClBGihU7uff24Jm82wrwslKSFl69YShOqeA/LqhDC5ZcvuiISqqk9Vq2aDNtiUo333vuan72ecURbu8LIhdalo0RIof70wqvRDwEoK6FzwPIkEum7dSoEUe5hw8NM8dx//LaX700+/wIERXPJHVwOiTLvzTktBVO4Yf2XixNLkAmiW7+/d5ZdfgEHqInh3V/tFBvq7VjkGBw6Mw+D1UMSvt2rV8xCaG6Fofw8CkROFUERtp3w/KkL36/Puu7O45l9BWg1DEC9n7TSiIt7/Yw2fsn8XXngW6KwoE9UdyGkXqJOKfjrzzPIYJJj8CnGpW7e2sWbNfJCHoT1v7kOj5+6KK6oW2OcqNTUdWqQtJLPdCPVZPMaz4ijlF2Igq1pgo1bOIMkFrBIRKcqDUa5cqQjc36hj/bE/B5UtWwp5KXL5s/7+uyRraCsM0FcRUZtyDNAQzZp6ei5H8YwZQzB6bYdO4xNj5MhVpnFbUVPK6yYDX/361QGStDCfE38DrTxP+hT2smrV7xj3CZ+LQBEN0Vtvrfd45oJ/fgJ1JSameFiRCe7tK6pIoJg//tgKresQ5Or90AwmQZOafZzCDbusmXS2f38LyPTKK4qCdm/F+3fpCl98UcL47bebPMbEe/38trV795YYB/fhUFgALXCKmVMxUB8l8777bnGci5dghPyE9zMwyGHDhvXoRSDEvBQBv0QbbAfco8OlG/3ySzaguDeYKw6Tu+BlL61am2rUqE5C4o9Innwfc8JBHIEoVQGK6Gz79o3DYXA1c8wEW9cXoEm33dGsXWfh2D0Udn4b6YerV2dDDVYr17MnZ8dHH03B8fMuwLxD6KhRpnE2OTka+SITkEWWqWuGagAeMcKiKwyUs8nKd1nCeP/9RhiGo5mjUwFzueYLt2EJ8WtsbCZO8bTjTjBFw7jrxCE2W6AOO8QgKyeKABGRKNWrp0CztAGnQw0cFpkRa1d9s0AbgR0gcpBUrFice9kT+0db9PszvF5amzbtsNO0Ze5eAWBzCXUXo2+k8KyXxyHUEMDZtUQbXZzr2Dp1rkK2VGL3XLuC3rB8eWkiTa/luCxk1Mg925JpNNe6iiKePvvMcoIIHOIUZwQK8gg4DpCCfPcKb99Lu13apTg9riMPCJi642Ul31oQ/TGD/81wlnRjgXmLZM64IyJbJMyojBr1EUZItBi3IsfF448HXkjdDsn1Vc337GkYM2danOmuChLaFHYtNJjyc4hD+lhXXFWO/xfNgjgr5URRBIi89KEWocyVxF0oJ9ENKCpFAqqSX33xhYGx2GrZJfjWqXPxccFPezIzsxEYQj17zuPkZMnIUKLxyC3oOc+Qf35df30taLPuA10xDWRpckDaMlfPhYLq2bMk9+haIn9u9ztWAweOhr88CUXcdXTw/xX+36iRxQMqYchbEY/6gQPlTJqbgnDv3nuvF8JjR5S4wyEphAMGlECR+y8Gnou8DUfY2wYO7MyYr8QhuMeW8ux+wjVrrNxEnkl3k5NTUfh3gFjqinH3PZ63ksfml8L1run5a9bseubX2bxfgQ0P7mPn+X3ChBioBe/2+455HhPub9F2iO5l9uzFIIXXmYZWGdJq1boQB8WVoNIbRTzqSFRV774bg1M1fAfI3Lml4FBueFLHLNwx1/E7duwHCTwBg9hC0whZs2Yy62EWdGoykpVijSyKwn8WhruHUGCxhhWgcuWVNeGh/hpUcU55JpRLsBxcl0bs/lpLvb05SO/2eeedYfz9d2JEuLRXrixKVJx1Lcq/oY+/ctllF5AstZtZRdEoigxRHoaTtebpnHv2HMQxJ9BEccbgTDMC1V+fT9W+ffsOYHSNzNllhzp4MNnrOAfz/ATqzWWXVQEBvC4sTnvXOUSBcvRoNjJaDxDFSV4dG+Kc10c89QI1iXddcrfkf+u6XK3l/D9oUAzt3oVRt2TOHQXkl96X4cOfxpF4IVE9E3EQJQGsyvYZqfHrr8pVGItB/U7oiIex/rmri74vOu3/2TsPMCmKJgz3RcIF7giCAhIUFBMK/mAiGAAzQREFSYIikqOSM5IVAYmSkwkVQQQUEMkgQUFAgoCS8wXgAtz/1qwLe3sbZmd3LzH9PHe7O9Pd01MTurrqq694eYeFoTDbFXF6SETH/Plpc/nZVU31UwAVkybFqXr1pjMnPMrYX0i13/ZHhQoVADetgT+/EfS5+6ChvaDlg7S/rrLumzs3lMiGMBxCH6hmzd51eJ/b9m3ke+3ajTnffkTrezcH/Pab5AIog1xTvyt/JvFk9+7vAiqKYT1z2WGeDkmuLGA9Add5muPu118t61Gh5HFXhg4NJ9Kk3fV3o9wvMTHe6YK2x4yJCdYiBG23pdccYHvMjPyelJTEHKRv3tYzzty5r6JrWZxKBQveBtiSh015d6/KccVuIQ5jWTeLcd9dKVo0gjVXdYBPt7isKte7PEYY+SP7qsu61p3PPltXzZr1PfrjBesmQ59yLitXBpMj6HGuQU5sNhhxfFROn75h87F2KTYioX2XZ9AspgSysgR8ZKrMyiIwx57ZJABKI0UMP1JY5C23c35Yh5tCvQE4R2qyISffRfucZt3pq09ZZAoa8PLlmFSLbJlIRWkWFIq3ReiFhF9R+rQqxIKmEM5ZiRCRhZAYlYWayrrfekyZ/MQjL8mpxMj53ns3EA4bNyr4Zy2LKXFkSAijcEOiiytB8ztDawidl8z3kuSzePG0x5RjC6dtqVIltIW3yMha8uYNAzl5zieLXYmuyZ8/r9wD1u6z9We7dvW4RjlZSM0koiBWczS4OmFZVHfoEK5xxA4Y0IKqAU5lde5cDEit3SDRXPXofp84t+rVUyC7CL16O219US47dgxX06Z113ZmhWsnlCNjx/aARmEAi9M47Z5Pe2ZptwgyZvjwHBjgyoBcbOZU9mlberYlV64cOB9H4WTpoA4fPoNzMtnps2vbs1ADSESXLKodFUlKffjwKVCMo1HEP9DeP7yFHFXN0ttatqyLQWUVjhDjCXiPHxf+25wYvp/y23W2FbI8NxMmfE8i8TnMPYm823mB2xRJIv7dd+u4vhOhR6vE/ddCc2LZVDH8VQypefPehmMsxqGhTm/HgmJdvTqEZ+sRv8lM3mtbt+7XEqonJ19j3olizKWIpixk2HD02WeLyHs0nXdrLEYcR4a4S5oIdu6MgVJusJox4x7m3/dxiOgzxOmVn7/qVap0H4b+EJzhntEgOhrPkiXi4Krkg+sboOk2lqle/zuoVq1nMIJOZ85x7ayTyFrhrhY6B9hKNAOIHEtoh8Rm8TjsQSkpEeg9UYbPxZqE2p9zHvoueQy2YSyeD4r6b03Pio4O0Iw6//xzDSBEPoyKtUGo3029LUQF7ID68LT2LBQvfivn+hCOr8fVrbfmc3Q5/bYtODhIk7kvDiBLg8BAe13H2P3jajwVKz5IvpefiHq1PO+u6rrb9+mnQbwfjmDcTtQFQJEIbjHuCi1tz56KJNqO9fAffwzk/VcQB8Ibhu9bd2NPr/0tWtRC932Md+9snETrNDowcTxHRV0FUR2AMypMy7NWuvQdUPW+ylrmPYZmfx84H63Q2O3dK1bPpOuVhK6ub1/LGkoAV54WaTN2bAxAhLa8QwSM4HwOuO2224hWWQ712Xqi/kcAMNjGcxigrbUkivjgwWuAx8KJDGpIZEtzwGaWZ1Tv++Q01solZG7fvn0Vet0B7X4oWPBWaLuqYMR9VpUseQOgU6/em+SVGI4j5oquiBtHcpH358cf56GP91Pde598MgyH0Fiimy6mWjPb9yGOPlmXSm7ByZMtQDv7Oo5+C1hw5EhF/+6peFesUORcKsb90vL6GIuQ7FLelTJ++/W0o+O527ZzZwD3Igtrm6L3mtk0ydJfxQF28aJNqICXZ3P+fCgyzaNds7KE/H3zTRg9eu8AOXzY4uQUEJ+wWYiNxVUib1nPSu4df1zPymQZ79YtDLDNBUPAO6uIv/suAFvBM9ffPTlzRmKziuO3tYbxT7Fxia3IvggV4Mcf2281f5sSyFoSwJxlFlMCmUsCODNirQ4QRrbD2ej27NmzjSgQsQyF4YG/31k9b7aLUWXbtgOETqdelEu4uiyenTkRPDmm9CF9HT1qSURu21aiMGbMsOQZkUTr4gyxLZLr46mnLA4QMU7LdzFOi6Fb8klUrSpctZZJXowAO3da+hNUmWwXii17JVB+i4IgKLQSJWyPZvkuiuOgQYEYh+qzqE2t9Dz44F0Y2v9B4U7bztMt27fngD7lgTTH8LSfrFS/adOXQIaVA/U3CuPnEWjQ4jHoXWPxYrnXhOtTqNe+/TaCUO5oFr4d1WOP3aMZOERJs78e1nNfs2YX11SiaaxbjH8KKnHEiLQOEEv+mdygEd/geSnhdCzGj+y/lpUrl4VWow9OkME4dmJ5Llzz5O/fL7RjYSjpFYnUwkrBQtyZ7H0x6gIFojHGfEry3DEY8zejuMdpz5h9XhB5NoV/XnJ+yLMuRmhXReiwFi/eiENzL8ax0n49B1fj8Oe+IkVu4brWwoH8JQZrzxdRYnBr3z4MI1QbUGOhfpfRuXOxXOOeGHz+4fpdcioacYzI38yZy7Vr+M03fXlflnJa35MdvXu3JNlrD/qNMxzR16dPbmj96vPOCfSpzOQ9t2TJJua42USaneD9mAIKNZ5xphAlkxPHUSgUTyHkZ3iZZ7km6FRBD7ov0m+vXhMwkC+nf/dULMLTP2uW0Nj8BlK7FVE6H0OTEOX+QBlcIzo6kvfzXaAGt2j6gtHhCMf16tWhvDcf8fr6WihDZHJyPoc5Gmf9+jUwPM5Dl0nkOqetATCV8Vl0olq1LAARsVVJFKS8K0WPk/elRL0GBV2Dd/yYKl68UNqOMsGWv/76l/McCGXWGe7rOI2Gx17/XLYsFoPux9o837ixIDVTNN1SzvXQoYM4RDYQVTwD8Eop3oXttaiR9Dg1oQDZt28tugoD8bLs26fQhwqluueM3j+uhlKt2sPMt8GaA8JThLptv5KoOSHhKkj4qx69S8W4Ljq/XEcBOUlybmuR6/nFF0G86/Lzjh7Kvexf/cN6XH9/3nZbfnTL9qwv3kN/OaTRD164EAPQKgeRMSUwZN+hReJdvIgSShE56NW7ypS5V02fLnOBLBktRXJ+SHQ9vgnDRdrWrBlHRMVsdF8WVW5KhQqPAEL7SjOqHoNf+Bw8guI8vf32Yhp9nrW53vM6Djqjf/8OyGsjzs1LRF0k2qwXdiDD5eQpGcb7sTgJ50dDK3Q/xwkH+NIN4F1/QHx4gQyUb74JYtzlWFdUuX4NZs+ewpwwBqd0jC5HX/PmlrWvvJubNLGsR/FTOSwC+JL8LKKPyfPghJHoelvJVdmrVyEYET7XaHzEeWwtJUvegby2eJ2zQkB6166FAT6J1K6nRAK4WodZj5/dPnPkyMV6NESLkPWGYcAqlx07cjDH3avdVyVK3IFzMEgDeDpjHbC2c/cpdMDyPpV7TfLH1KihmAcVOWwctzxw4Bq5PYpdv78d1zK+tXfvUawt3sExetHQ2lxy7A0fHo2DqP/1MVapUg39dYbmWDQ+MomyFJpzx44OYQWR+emvv5TPKM+9GavZ1pSAEQn4wBxm5LBmm5tZAjgtUFu10o8E5n3/+57qgzqCW45CmRi2b9++91PttPlBvX/5iftASTL0d2x2+eRrYuKPKd9/v1798sswFpbAAf4rEl0hFFWyuPZFadPGElYo0RmOiiCEhCtSjmdNkCictUJVNWCAZWEvDhBBjglwqE8f5TIK48wZpaHKBBExcaIl4sP2uIKWlIWmJHm3L199JQm3i6LoTrbfhRFoA8jk4Rjmbywy0lTSsUEWNk8/HUFfE9IdrahjeOlS5e+/T+DoWI3DYweIrhOaYi3IzfLlHwAp9/h1yiVZ/LtTvD/8cAYLhnlaRJG3gxejkiiM69bd6EkilCTyQ5wfLVvarNRvVMkS3wRp36/fRGiH1hNxkYiRKQHqKaUZLuSZ2b49AOUyHIRNHp6x97hHy6X7eUlOggkT5qulSzfxrCdojk4xhB06ZFFYZYEoxklPSs2aj6FE972uRHvSNivUFYd6vXrdeXfu1qid9DoCJcKufftcvEufwvjCS9rPRehsHnusLQuPkyz0uZAelHz5Inn/DtQcWR40c1q1e/dxIG+X8zx4HikgxpG5c+8AxTxaezc5PYiHO06dukCUI949dYDrGO/UgCHP6vTpkrwyEgNPN5zK97k90uTJ3/HczyDy8RLGcLfVU1VYsiQQ2rDbcVKOu061kapCJvshxvTXX28PUjdOiww1MrxWrXKTeLk5xoTnjTRP1UbPHJaqgc2PMWO+xFk4l+fzhn4muyVqS4wdAC3J7WFxetg0S/N15UqJxI3gvd4KY3PVNPszcsPixesZ2yiMHbFQS6YdiehLohsKOlqMhBLF66xI3eXLA9Bnw9HvmuMcxgrk57Jq1Q6APAN4FkHheFlmzgzAGPY66Fku7n/F1f0jc/q8ecvV2rWbAPUchopKOOUDmdcLq4oVy5GboQaRM3mtXaX67N9fdNzv3UYYpWpk80MQ7o89ZonmEJCTkSLvMsnLB7hf49kXQ/CQIWEYmO8m8uwDovUijHSbJdsEB8dw7Z9BFxZDs2enIIbpRx8tTWTUGQzWlrYC5pk1S7HG8Kwv+9ryrmnUqATzB+ikdCwLF34FHVhXwGgXiVS65tKIKmjurl2jAFe8DbihmzbKpk1rAyDYyG/PgCGi+3fteis642ocUvm1vg6hgDZo8CR66QWP0edibBUK51mzAtC5IxnTZXSuRJ5VSyLptWuT0WmvaesOAfe5oi8S1P6wYbmJ9ruNSPRvcAAXSXNFfvhhMXP1e6xTY9Ls82SDRIBHRvYkCqY1awT36zBP+s5qddu3b8x7aqHX4Edhsnj22Vt4X++5rjt+9NFgDO6jAdRgCDFYhL60alXLu1gYNqQIAKJpUwuVuThCbYvkPm3SpCT382+2m33+vVOn5rzDlzC/X3L5/NofWACH9etHouOMwPFZ9/puGFNYgz/JHB/nUX/XO/jvy6hRFkejNQm6/X5xJsmcNH68/R79v8lva9qg9YvLrOljCWA2MYspgUwpgT9kVBh2S7gZXR7Zj3LrhOjFTWsdu0NDQ1DEUj8qYkDzVAF3dSgBp7gyyonCJ6h7a/Jp8bw3aaKgsbAkywsKCgCBYaEnEkXSHd+y6KwSwig0RpJsXZAstkXCQiVixL6I4tu9ewCK6kD7Xdrv5577HyjcUCXIBG+K5DUpUaLkTev8ENkJjUuHDq8h60E4mz6Dc3MqqL/h5PxoeN35oVfGksDXqvTpbeOsniA/5H4VRXX5cln4ScLzIoSxD8vSzg853zx5wqBg6AgqeAY5Qdrj5KnOuZXmOpTE6VAOhFNj0IlDuB5TMsT5IWOUvDsTJvTAcJmfaCuLkipOSaGlk+fOU+eH9Ll69R84PzwzuEu7rFKCsGjPnTsQR2J5UNFhupJAyzv25ZfDSVL7LAan1ulyqm+8MQhHlufODxnc2bMxoFn7EDWC1c0HZcCAd5kX7gcBnVObW/R0KXPirFlBLIoKIe9BMn/raaarzuHDJ0HstWKxtxPnhnPnh3Qm81vnzknUO4ujpA8IuxUuj3Ho0AktouSTTzx3fkjHzz13jeibkxhU5rs8TmbZWbp0ERbOdTHs5kqV5FLv+GbPDub9X4I+ntPbxG/1WrV6hWfmdiKhQID8V4S7WqgaBcAhFEI5clj3OP988kkFmj6WaygI5pXOK6bznl9//QNnxUhQz86dH337Wqi9JJ+BK+eHDF0eyerVU3B+xWIgnEQeA7wmfi7igNy8OQjggPcHmjcvHHRrNbcdXbmSiK46DudnE973U9F1t2KgPsvcfkGtWnWO94IsMWZjtGuBAXMgVJFpB9e1a2O1cGEUdGNuD+ewghhrhWrFqPNDOpV3mdzLb70VzHsmkjVASXJDdMWpM+imcn44FLAHG2UuqlXrdeYCy3tCDOViFPXW+SFDkD6uXLlARCadplOZN28Gz25nQAbn0VNcOz9kSJK4/ccfL4DaHs+6sYM2yokTPycqrAIOEMmL4X7govdPmhTKe7UIAMAfrzs/pOWAAR2IRPHc+SFtMYRq7+lp01KI6ryV30NZ65THmZsTIMg1nCPCxGBhSBB2gkGDLDkIxHFyBd+NvO9JO8KzEYaTJC9OiVbIZb1D54ccr3r1GmrHjnyGn2vpQ6LxFyyIBEjQSH7e9KVx4/aAR6K8lsPcuSHYJBqn0h2bNWvN+y5aHTlivHuxn9SpY3EiW3spWlQx11si7eT+si2S8++NN9613eSX70OHTkB/eZz5KEyjhNNzEAGuvvJKHuaurqmcH9L2Tqg/Speugj5kXPcWOU+YYKFBdzael16SXJ8WJ4izOq622wRkuapm7jMl4DcJpLbq+u0wZsemBDyWwJL/WtTghY45Pm1h+71sDf9vz/a0NXyzpVSpwnDHpoaEysQJ4MVnRfqSPl2VcuUkkaKF3kZQC5Lvo3//SFBgvdmeotHeyATvSZEk6927K5LEWYza1rbi/LAN+7covhbarHfffQ2jYEFr1VSfYmh8//0moBnCUm335IcotD17hqPMtvSkmVnXhQRCQ3NoPOEuqujeJUZOWXi8+mohFh1VQdf206KBypYtqbuPzF4xIiI3yu+TcA13JP/GJyBdPiVkejBK6uuaAyKjxy/OCjF4+6oEBQVC/ePF6sJXA/FjP+LInjatD0bbdiyE8mq0VqtWpXYYnTxpUeibNInAmVcIVHl/IiBapFqM+WuIX3/9K06sv7xyRJ09G0ui1XE+GaK8y2fM6IuR40UMb+FEQbp2+gsyuUGDMBzgD/O8fOLTnBiXLl1hwdcFR9RZFnxYYXQWmHcwUsQDHvgUROFOp62GDp2GUSfWY+SqbYfdul3m/vqWRSwTWBYobdu+huGuKsbf3LqMX3JKogeMHRsCrUgRHF0D0uW5cCdKQfN//vkQ0MjFMIzl0Iya7doJRaGFEtRde9v9AhKYPz8eY96nRGGdsN2VId8l/1ybNh/iyIvTcgU4GoTQwRw4YKFB1ePosfYhIJfZs+O5Z2epjRt3Wzf75VNygDRs+AJGFZA8XhR5X+fLVxTKu9tc9rJv31EcH82h/VkKcCSWeyERVPmNnAEC4pBo6RYtrkIFF4vTaD2RQm/z7uIlZlMkufz06f2pF6nk/eZJkQjtGTMCaetJK8d1JZLp2LHcGAHHgUj+FJqjio4rmltdSuCtt9pgwI/U8qJJhLtE9/qq3HVXIFHzdJoO5XeQL5Mn9+VddVFzHug9pLwfxo6NAwiyAMPvPBzDObi/v0Wn7QFyPxrwTw51wsFrTyKZPv88AMdBFA6U2jiK10PZdfv1w56g0cGDOwAFXd9k6ItESyUkHOPchhNduAsnxRVo4JKJarXksBTAnzg6hJpZctzVr295xz/5ZCDndT/n8TF6yi50u+5EZFgcXY4GIvs+/ngG79Yo3XOfbT8CNmzVKhLdcAzOFofmCdvqN8X3hx56CFBoKd5Pxg3vcu/NmJFHNWmSeu0vMh48eAJ2ijyGnOhLlwrIKy2FuFyYKHw2AhrtgE9QrqsUGce334azxn3DssGP/+VelJxepUt34fmKZk4OUhLd4ajIuIYOFcdMQSKwpuEIb+WoGnrQWObaAhoNssMKLjbKs96ggQUce/fdzisKkKJq1SCARjm0fLjOa6bdI3qkMEaYxZRARkrAdIBkpPTNYzuVQHJy8hx2ynQUCXKnjaOKLHzbynb2X8qZM+ePjur4Ypsg8SVU0jo5Sp+C+JGoCXFIeFukD/GkC6+iu/Lyy4oFm2IxlwuFtBgomZEs1I9qtAivvuquteP9woMpCwHJJ2ItQmkkXNkyGUpOkerVLWh/QRf07MlqzEVp0KA6xvY7UKydK6DOmsvE2LVrLhBvNaGrKe6smrndQwncc89d0IR4Z3ywHlKeBXF2rF8/nUXHB9x7lhwk1v3mp/8lcPz4OY3Gw1dHErTs/v1HfdVdpu6ndu3K0MrNIlqiK4b6KqCJC3EP5yHZXx4WFEW4r6uxGOqDMWyadm+n18n06TMDVD0vXC9KUlIyKM/NzCeEZ/mgiBOkV6/mGI2GYWx5GHRlBA5zMcRZHEVClThwYE4McpGgSu8gN003FrD9tEgqHxz+ehe9eo0HFXheS1Z9faPOL8IbPXFiHGMbwsIyrXNCjMxr1mwlN4LODp1UE8BAzZqJzMtYaLJAEUT0sGFtyV/SBMqJCJDRAZrzwNHQxektuTJq1QqHWqoKBrCPcRbldlQ1Q7aFh+fS6NZy536JZzanxk8tyHsjRQwigwaJI3GIkeY+bTNs2EzyUcUAOHHcrQARhPJKonl5VD0ukjdlzJh4jHkfosvC9+fH0r7961yjaENGGRmWINSFouyjj7q6HOXBg8dxlnYCvHAKZHuiRl/pqoEYcurXv0rE2gVyJfRJ4wwSPXT27CFEkOQF4RzsNmJKdNhp04JxtEdhHM3lVfSHddyCkA8PT8lUz5x1bFnp85ZbbsE4PgDDd4QW4e4ob5DR8wkLu8a6x4nl0minDtoJNWaHDo2hvDMWbSHvidGjY3F29EJPOKM5sZs0aYHhehtRxd0xPt8JNVw0+lE+DMD5cAxGA4C4Dfq4JugBK3jfTOBeTG28/Pnn5VBwxdOXgwF7sEkMvJLzZdiwY1ynKw6fXTm0OD6Eokci3oSVYPPma9BiHQYUdIx8MTl1HfH+++8HRDgQ/S+PFkGiqxGVJIqtSZNI8me1xGlUQ2+zm6LeqFHToJCM1qJjPD1hsa+8914k7/gRgGdAItiVKlWqEgU3QIt8kNwUeoroLZJDdehQS86PEBzfjorYOh5/XEGZaskx0769jGNkmvvcUVtfbBMQR4sW7XBKrsE5+Q73Vn50yXyAciKJ/gzneY/CuZgXx8ftAAD68KxuwfnwpNNDR6HETJ36LQ6jfOi2Tqul2SFUfs89pwAPWGhD01Sw2SCMA3Pn3kIk2FKcGYW1yDCZ+9wViZySZy48/BV3Vc39pgT8KgEvpyu/js3sPJtKQE8OEDl16n3Ixwf8ycqsQ0RExMTffvstqXjx4jlJRvs+2/ryJ6U/+T/6WL769r/kAJEeO3YcBbpkOchh7ad2EEkkLhOFOCW8KTLpihInk7S7IgmwW7TISeRHe8ZSSVNey5dvCMr1rNukcK76lsWlODlkshTgjCBxZDIT6i3J9xAYGEyodGHoAEaxqGPV7KYICvbllzsykf+D4SlJ1+Jcok46d84FoukxFPTO2rm5OUyG7z5x4hxy2Yfx+F9CtZOhHssPFcqd6u67i/rUQO3qRPVwzx44cAykb3sUPC60l0WQYIcO1QQl+66XPZnNjUpA0MlPPNGOhZtvDN2SKHratM4YOHmhmSXdJfDnn4dZ4HRmAc/q2ssSGhrMs9mERZNBj7iL40vU0aZNe0Bm7sEgKclpc+IoL818cY8WFeiPBKD//nsaY0xLOLvjdM0jzoY/dGgoKPpGGL9Sy+Xnn7di/B9ElIh3zic5rtAoTJz4EAZTUV2yTjly5BQGtTk4BNdBrxhAbpV4jBBXoRcJIDIsjMikAEASd2BQboSjEGuBj4ueOUzvIR97rCnox+NaImC9bRzVq1kzAgf/SFWmzO2Odvt9W0KCRC28CeAlxmmeFkmSLUYdiQb2pnTpkhv9ryPPmX/f/zt2HIQy833oOWKJ4tA/YjE6vvlmGA7qFhg/UVTtivX+ESdOpUrv4Iz9B0eYXSUdPw8elFx80USNTEmj54qjtGfPcSDMNwHQuYxenMy9YZG/gJgk4uOXX4JxFOfCmPYw/byMsbgvQCDfzNGtWklUYj+/PH86RJMpqniTA8R6AjJH9e/fFYP+XCKELnF9rHu8+2zaNB/rxG+0JOPe9eS69dKlP+JIbMH72jtdftKkEObwtkRH9UxzQACI0MadBNF/jfVYPt4/rp3dnTs3h47na6I20nSle4MYq4XqrUkTZQiMIOxjzz8fxdpxIfPX/bqPu2TJ99wPHbh2FzCuX3WqY8j4hPK3X79oHKXdeR81T3UM6zvIHzpQqgNl8h9r167RaJqnTr2gO8JK7A8SZVehQgstesfVKa5Z8yuOgXeYq85zryRoFIH29eVaSZ5WAQcIWHXwYJC0kfa1Uv+Wd3/r1oroonCcDHW5znjY0rlY76FrGF+OHPmXOWUPOlgCgKI8RCzeS7QK6AwPyqFDhzinBqpUqQPYVhJUkSKOG0vEyezZFiowibKS59BZEdl++20Qss1PFPAP6FklcQrGIq9O6L/LcVLFAoi6mopyW2xJu3cLPa5Ekuem7iea8zBfvmjTBu1M0OZ2v0vAvPn8LuKb+wA4MX5AArfZSaHsf79P8gnm40YBlfj83r17j/23JZD2M/j+5n+/L/B5nDrFUDI0jYzPOYULF26yatWq5P/q+PTD6gCRkPoGDdqBOInTnAJykD17FAYBS1KtIAPoO+lD8twK8lQWsq7CDaWuFOE6bd78dsKAJ2m/V6/+HYNLfw3hqm3w4p+EgMpYhK/4XWzbixcrUNCCtAln0VWJSasFinBO3Ue4fDlB42Devn0deRTitAWpI4SQLB4XLQoAsReOkf51Fnl1MrXzQxTcBQt+JWx2DojZ89AnJMO7eQmHh2LREIpxMCfh4EHcL89pOTH0OIx0C9VBRavSJONKTkbTcFKefPId0JNHtKTZTqro2lylShAGplFwCt+lq75ZyfcSiIu7zHVsBBWZ9wZzGZ0kUv366z4Ybu7z/WDNHt1K4LPPljCXjCPK0DfT2FNPPcRclX5GeL3vILeCcFChb99JOPe/A1HNZOlFkYjNunXzY8yflWp+GT5cDGGzMMyysvOyCKe8IPW2b5/rZU8Z01yM7jt3HmL8B4hGioEiJScL7+KaU1/yI/mr+Or+2b//GHNue+hRvDMOynl+9VUAdC8vYjxp5a/TdtnvihXbiLQayJzt3DEnAJxFiyw0Hi47c7NT6J369SuNzveJm5re7968eS9Gkt4YZGIB8bjPXSCgn06dwolSaYrB6wWHA7DePx99NA/day4GzbSRXg4bOtgokcv79lVBJ+/iYK9Ea1/UUK/r129hDSDAF0mqHoT+XgQ9uTx6Xw0crVG8Z/ZipO6Fnuj9vSgD6dJFcp/0xMj8oMNx3QwbfeEAETmJrvzppyNYN31IHgjv3/vS5+OPR7Mu263RSslvf5U333yOe2EDgDzvjiBG52efLaglm/auJ6HMqQboYotHTk37Ywql1bJl3iVV3rFDGARK4cjfmGqOtz+W/e/TLKyHDOkGEHEFCPgEKLcuaTRbsl4VxPq2bTl5z+biGS+vRSgUcxCSZ30HuVuH2R87O/7eyku7Q4dGOCnOEDmXANrf8VmK/eOHHwKYY6N4v/fiPmrquKLd1jjQkrNnf8Z7eCKAzRM4o1O0vKdiS5DcfQJEKV3aQtntiSO8bNkAdMR3sVv4Nn+d3fCd/vTHPSTOlPnzZxMRMgr7zVnsFZcBLcmcZbm3N2wIRucLgfI8DJkl4AiM1egi7QfJK1NzKn3ySR5sYA8RpSV6eYFU1Y6QPGTmzE9xciwlwu4ijhtLbtr4+ADWqyU12T7//Iu0D9XamQ6QVOIzf6SzBMB6m8WUgF8lcA+9F3NyhIJsl7/rBeeG5c1o2XKNyI6Gd91111coFQK3eJi/O/l+ns+V1J3E/oXpwbsqeUBeeulZXvrfgwLDykERhwW0l4T+KRbd2iaP/02cKHRW+pwf0rl40jnv68dZu3YboZBx139780UiPYTmQv4kZ8TTT4eg1JeFIqE+yrYHcL3/BpErVw4W7x2ZNJ+DomEmqI39GM1TQM3FM9mmEOocxOIjDGNLCjQcFTFavKkl/vbmHPzd9ujRMziHBoLEOAIC4pKmZKU+ZiI/E7UE4bNmfQEacQkOpC7IkoucwUXQu4MHf4Qi5Nyg4m6IgoAKDb2q0ckIDUr+/HncNTH3+0ECQvmSI4eTeG4Dx5N3ykMPef6MGziU2cSBBDZs2O0z54d0v2fPEQdHyZqbli9fjyHYO+eHnDnsJ7y3EzFonCHf1o2F27lzZ5l3fGMEg1KdOdr7sWbUlcqRIxTjT2ntL6PG4M1xxbhesaJx47ftsR95JAWKh522m9L1+2+//YluCDTTSRHaVKHw8RAU6rA3iWT4++/jmmHYVr90WNnLjQKcWLFiIg7f4UQd/qWaNo1DP0pJdR6C5hYE78yZQrkWRb2ebilRxRExefI3GNS8u/4NG0oUySb00zjGlNZyJzpPu3avaX+uRCEUcTExN3R1V3X17IuJCQTFnFtPVbOOGwnIPd6qVReQzHNBWh8in4WbBm52S1LiPHkK+N35IcZ11rxQ0LoZkI7dgojPly+Z5+s4KHlg8l4V7+9zWUePG+fVIDS5FChwGufjFtaa/9PdmRhyR46cwvN6kXfTCii11gBy26e9D2+9tRj9VsIh/iQ6BEqEWdxKoBxGDaFiHTduGO/22UR2SB6XizjIUrQIG6Fa2r49nKjeEK7TE9BsDyI6oajbfq0VhILt3XfbEXVUBwdLZfq/oNGIC3tFxYoKO41lbrTW1/v58MMROAVfSWVj0ds2s9YTeq369RtBn9UQcMBBAJrb+dyNnpqMA6MIQIRyzK33EUkawr3/M3aNYTA87AP4EoienMgzoKDlCiUH1jVsXvch967Mj5Ucnq7kBerZc4j2J1Fk8XCoC42uPWWew8bmRlMC6SwB0wGSzgK/2Q6Hslbc23MmIuQ7+pC/DC0ffNAEXsY/WGQdZNIFakDp1ctCgSXh/EIh5UmRxFzCof799/pbCfdlsWKFrjf4/fc/WYhd/+nVF4ka/uQTBQ1LDpS/j0AJ3M6k6P0romLFMhiwPtQWlNu27Sf0/G9CJq8QIhkJz/YdUG6UhErF1u/l1Wn4rbGgS4VbunfvC6pGDdcGM+Gdb906GRqw89wrg1hkvE3I9LN+G5uejmvWfBzEzPco8n/A7es8UsRZX2JwkfDYL78U5fUkClV3uEjHpBvVl7Nx3azbn3mmHCion7VFmrcyKFAgj0b54SqCyNtjmO2dS0DoVXxZkpOzrhHeVg5yHjExcR4lerVtb/+9bNkk0IH7UzlAgoNDUuX3sm/j6W8BKZglYySwe/dfLNIFhOB9kUjYo0fPet+RwR4OHToM6Ma5niFRG9gtfFIEU1O0aCDG4FMalZ1POnXRSYECUTiXBkHx8Y/67LMFIPG3a8+55NGRKKrg4FCcP6XRD18BhHOvLoPUL7/8DlAnCUO0iwPr2CXR3LVqXSYPzGyu/zktkiM2Fo8MRUAHEhH1xhtPgmatzG/neqskat+/33cvg927U9DJi+k4A7OKXgk0bdoRQFA3ePaNg4LkWNOmhaHft9Z7WMP1ThHGWKhQAM+D4S5SNbz33iQMm7u8doAULlwcI+lmwxEg4kDC/uo1baGcXKNGF1ijTPbIAWIVSmRkHp792tqfdZs/PhNJeCG0QbK+joiI1PV+88c4/NlnLl7mnTv3wT7RHWf2RtaMmwA57mS9chXD++1Eyz1KtMFjOJk9o3WyHfPFixdhrAiCcsl2q/HvBYHjXrhw3ngHmbilOH3vwLLlrYIAAEAASURBVFAlf87K008/g8PqGebgBN4Lf6pjx45p92bt2kUArpbRnCTO2tpvl+TuQt1lFlMCmVUC3ls3M+uZmeMyJeBjCYiy8tVXQ+H27a0ZUQYPvqSh1iQhrCRlE0qsVq3cJ6OUsE9BuixcqHAMKKf8zo6Gv3VrIAvDG7HPYjhzx23pqB9H26QfQWZMmNBdS3LtqI432wRNJ+H7WTGEXyiHXnutK5Es51Gs9UtBkGVffhmPw2EyCJdbidYpq7+xj2uKAjRhQg8QLm1AZJzi07lxxf7Q4vx4/XWLw094RIsUuaZWrz6mxo//hnv+Ffvq5u90kECnTnUJy9+ozp/3jgYrPDwnnL310mHE5iGcSSA6OtzZLkPbs4sDRN67Ekbvq1KwYAI0khdSdVe69B2gaiWaygJqSLXTwx8yfxYsaC76PBSbz6onJSUCpvBNd2JkFJqIjCqC9nZ1fOHtjojw3eiEpuTSJbwP6VjuuqsoaN122hGFqkMoAENCgjTUqKfD2LDhD3IReR8NLRHQU6cmYwj7nvGkdSQfO3aWKOk/odqZTMLfhtC2vuDQgBkcHISedAtOnljdXPjOzlloeMLCIomczuGsirndgARq134VLvoPMZrHGza+S+6A5csjuB9eNTACz5pc4qEPD/fdfBgRcRWUtvfgi7JlK5ML8TtyPhpzPgvNXYUKnsnCWW1Znw0bttnZ7gzb/jfoxRkzxrJu+on3Six6TaBKShImBHH8FGeN+A4O71p+jyJKbwFIZMETcDXKn69LSEio5jD3Vb9XrgRq7Be+6i+r9pODUOaHoDeRP7OYEsiuEshA9T67itQ8r+wsAcmBIU6Qp55qThRAJBEgOTWO0K++suTnkCiQzz9XJK5KKwXZ9sUXlkgRyeUhkR+eRNRKKOLXX4cRVXBDkZDxSPJwXxSiFVHKwkBTEENqllQS6NZtjIYs8sT5Ye1AHEsTJ8aT3G0Y18qCJLTuS+/PfPki4fkeDTInhLwsYvBwPwKJVHrxRUlQnzo5Ya9el3GAfIEy773h0P0ozBr2EhA0aOXK94OWBbLqRSlUKC/cu0970YPZ1FsJPPbYfT6NggsIiIeTe4q3w/J7ezEmfv75KjiXxxJaPwDE91DAAd9AQbFXi2wKChIjge+GcfVqAMbV1Grvww/fRWJ3oOc+KJs2wdP58H0+6MnswogEBE167pyRlmnbyH0nuR0yqggtjRi+nRWhv5Lk4L4q0ldYmI+8RwYGJVQdEgkslBlGyq5dezRaWiNtpY3o1927W8BMki/IkfPD2rfocZILpHv3qSBmu2BEdqDwU7lZs7o4U7x/t0yfnhOqsDrWw5ufPpKAoNSHDp0MiCePEto1T4u0ee+9PPQxBeeU99fZ3fElGXlcnH7gkrv+4uKCeOZzu6vmdn+1atVZz4Zpz5Dbyg4qHDhgydngYJfHm6Kj5b0Y43E7fzWIIdlKhw5vkeT7aSK4pqvvvjtC3pXzUPWdxXF2Dt3jHPmXtkJN1Blw4ANE1f/gr6Fku34lF8vff/suym7v3iAShpfKdnIyT8iUgCmBtBJIvRJMu9/cYkrAlICdBARJ36TJ82rr1tlEA7QmB8hDGIjzkgAvEjRHuPrww5wYQUIwTuZi0RKp/VWsGEwovyLnhYJCS2GcUijMdh27+blypbSJ1oxDGzfuRmFPgALhbiil3DTUuVv6qVHjhnNFZ7NsX+3QoRPIfDOJO5MNn2uJEoqEcDHQPeD1yuAii/WiRUPVbbcpFG5JfmpJdm91pAmFiySSmznT4qybM0dBiSYJE1MPXNCnTz6ZSCQTkEmzZIgEJk/uqCVcNXrw6OgIHLq9tVB8o32Y7byXgNC85M7tG3SvIOA7d74G5cAPRHzx4GbCIjRUr77ahXfiuyz8P4IGcRH0gmuhkVzJvDYZBGdPOKMbQpOzTDPy+opWat++3CRjLJxKInffXRSjeW6XxuZUDVz8mD07AnqcF1zUMHf5UwIPPlhG/f47ngEfFInolXsjo0r58vfyDDs3Tt5zjyJ5qW9GJ8b/f/+9looazjc9p18vCQmJHuvUtqPr0kVBKam0/G222119lwjsTZv2AIjqDBI5Lfq9Vq0n2B8BnYirXlzvE9rbH38MIwLXBCm4lpSxvY+zMKtVqwMgkEho2PT3IXWlTZ06nVjbsbhLhyI5KE6cSDHsaLAfoiQ/vueee+03e/xbxnXXXeUx3huLThFns1Bg+aoEBIiMUrScB4sXL0bH/ZJnaAnvuH+07b46jrt+9u/fr55//hEYFRaR4P28qlfvWirGBqH7k7UW6RJwkMQxzlM4TFuSaL0NORp8Z9h3N86sul8iFXLnjlInT3p/BgIGPHMmAIq5Qt53ZvZgSsCUQKaXgEmBlekvkTnAzCoB4f+tX/8Z7c/RGCWJoiTNlhIYGIDy0wXOyxhyXziq7XqbGKc7dlSEa5+Ak3gEXJoh2uI3R45IEnHnhI/YMQLNda+p9/76ay6SkVdIvdH8hcNqAc6PeJd0FHrE1LhxEga/70A513VImaCnD1/UWb36d65zgmrTRqnmzUmuQ3YdWfiLMUUUcinisCGPneasA2TjtDzzzGUcf+sxZlZ2Wsfc4T8JCK3c0qVDuJ5d1alTFzxa3InzY9q0zloOHv+N0OxZjwRKlrxVMz6eO+c9pFsc62+8oeAkv4RDey73xiMY/fF2ZoIii/r+/T9TS5YshWM/TktYKVRDtqVq1WtEg8RCVRVLYtIZRIBchUZGwUFsW8vz72Lk3b5dpaF3tCTEfYNjTVQffcQq2GCR6I+kpPzkIbjDYA9mM28lULHi3dD5BWI88p6+atmyUFWlyiPeDslw+0qV7udcgjTjmCR3tS/58lkiOM+fV4Z0Stv+xEAvOSvkWciqRXLKiSyMlCVLFMZHZSiaWmi79uw5QkTtVDVq1LupDi+0uePH9yBKoAd6VpzHlGUSYdCqVRjUq10x9GVcdE6qk8qGP955px15pvID8ulFVP8FjR7W2aMg88iSJQGA2KKI6u4PNe6b6SYReT4Fof777xuZx7w7rDhwzpwJ8jr/h3UUvXuPQhZVoKE7n8rIb93v6lPeZRJ15Ysi+UQkZ0/FineyjgnAwZNAlEsyzq0QIvFDcSAFArZ4ExDjeyp//vy+OKTDPo4e/RfWgBeIaD2lraWslUTuwhIhbBBidC9ZUnILKS1yUSL+7rorRh048CX0eoHcY6OtzcxPJxKoW7eZmjVrAKCf/xavTuq527xgQRBA1lfdVTP3mxIwJZBNJGBGgGSTC2meRuaTgBgnJWGi/JUpUwzF+l2UrjCPF1myCGraVClBqA0fnkjY/RUWVbHq119j+X1U/fLLFY9Qa44kJYrYqlWhoFUqONp9U29bsWITcvEejSO6dv78yeqff05nqDx37PgdozeQK4oYSyW3x9ixcv0V3NaWv7lzLZRXrpwf0l4WYdu3A2EyS4ZJQHjUN2wYA4fyXSw8c7sdh/CIFy9eEETcYO7rim7rmxXSRwJDhjQnp1S4VweT6I9XXlGaAUIocvr1i2MhzcOdCYo4P5o1G4DRZTEGpDiSYCoMrs4HVqCAUkOGXAZhm6imTHFeT++e334Tx+7tGCHTPiMNGlQjAfRtasUKvb2lrif0kV26hPMe7Z56h/krXSUgz0+5cvfilPfusIJI/uqrHCC8a3jXkRet5T6tWvVh9e23zh+SunWVkghNb8vUqbk1uiZv+8nI9g8++ADRZJ7TZwk1bfv2yiP0v/15Xr6cyHX4iWjsQ/a7iAa/S7Vu3Qw9K4x3X5rdTjdIboA335TIj/rcB15au50exdxhlcDrrzeEYngNjqrqRO9HQ2uVA4oiC0JfUPryXbbJvu++q4YB+5d0dX5Yx9mwYTuiBODV9bJ8/nkIc6vvnDdFihQl4XU/1riRHtOJCbhh3TrvTkic3pMmKYCGCmfkFXJtnOManSXBfRxz8xWcCbG8S88SiXEaPWss+TYqcr3ne3dQJ60l+qRdu8aAPU5fd36I4+zrrwXEaIkymzVLQYWlMN4rTb+RKHtyhQOQEyrqBMY6mwhYkGlmcSmBevXeJM9mmDp71mU1lzvF/jFhQqTmFHNZ0dxpSsCUQLaRgANcUbY5N/NETAn4TQJCPyX85aLUFCoUTWI6LMluSt26T4FMiSfceob65JM4kCluGrCbCFoWT4o2Em6dtr7kqGrXToGmlcRvaffr3TJqVE4NrRIaGqK3yU1RT5IJx2PdglrcJ6Vs2UQl9C+3336LT/oz0smFCzGg3Yy0TNtGkFsXL6I9miVDJVC4cH4WfB+xMF/Hu2CO5mQTg3NMzGUtKkTeT6GhwRrHe4cOrxD58xyos9AMHbN58NQSePrpciyOy2mUcgkJWGANFHGy9u9/o+FTT8nvfVAEnCc5t4HQwxtdef1t6NCZOFy3QRF5xaXjw/5AXbsqjT7yxAmZa+336vst8/TIkWFEUTZ02EDyD0yZ0gcEYFscJOehtnRYzeFGcX40bBgGUrtxhlImORxcOm+8eDEedOvPas2ajUQUHiYiJpnIyUBQrYVxeJUnWrY619BHk4+Tc+vatRnH2UnS1TjDlEijRuXAOFhNy4nm5DDpsrlHj7eJ4NoC1WSskrnWvjTkdn7uOYvRTxyGRopEf2zdGg5S+VEjzTNNmyeffAhdOTfRFrEejUkMj2IA87acPx/Hu20e0bTd0nTVqNFzOKXDMLqOVZ06xRIJnEKukzTVtA1iyF28OIC+IjDkvk00HxZTs6SLBIoWLUrU8xfq+PGTOFF/4u9Xovj/1o5duHAJAD+VMGI/owoYfdh8cBaSb2PYsGjerzFQIBvrUCKlPvssAl3jXWMdOGlVr15D1rgXWa+OUJ9+epGoMicVbTb/848AHSLRWRPJu3MFPdVmp86vV68qLaJdIuV++kmxHnfeUHIyvv12Eu/3c7R5H3q6bapnzyHoJM4dzc57c7znu+8WMM/tQ59D8aDIM/3BB4roE3m2BQjnuJ0MQXQP+Vu37pp655222r1WrVrGOeIdj9TY1lOE+WzZsgVH8RaAoOeYnyMAh5bHSfywKly4iKFOIyPzEP0xkPfq+0S0x3qkW1oP2LdvGO/ZVkqceP4oSSAq9u7dQ5TTKe0+K1asOGCcEj695/wxbrNPUwLZWQK+e+NnZymZ53bTSiAx8UeLBoMExOHx2WffojSugpZA8igEapPtsWMp6urVEMKmH0OxeoWJzbWVZsuWvaply0FwnMdBrXRJo/aw17327VOgfBRRHkqNGKE0tKyziyDK38svKxQABRexs1rOt2/YoFTv3rcRSTLJzAVgJ6bz52NZtDYDLU/csg/KuHGBKLRtuO5YLXxUgoPlPgzQDN3JyWjabsq77w5E+V9DqLqbijp2S3TS888XIEfKLB21zSrpJYGzZ2M0R5vkr7l2LUUzOpYrdyeLjPxplG5P75/0Ooeb8TjiWK9atRMUFweZU9w/y7YyEpvMDz8IlY3tVoUhLRRDSQcWeE+m3uHDX+7uoZ07DxHF2Jn3aJxG9+DpoeW8JApEaGqwp3tc5s8PwkFYnrxGNt4hB70cOHAMVO/7UEpeYI5OcmqktDbdulXm3XD4uxvhVGQSvknLlSuJauDAz3DArmBuSUAPSSTyVZHUWmjBLCjqNWuC1Lx5uUHElgVJ3Q7apojr0nJ3/1yvqPPLyJFzMbJ8qUaPvqzpaDqbadXWrFGghm/lfpmEMS5jACHy3p406Wu1aNEa3t+XMZ5fARGsoEFKeyayXShVBEnsiCorbYsbW4RatVYtiVwapEUq3NiTtb5Z759KlZqhLx+BFlb/+KtWVbxv9dd3VTM6Opx1wudOE7mLI7pfvwno9VtZLySqhx5KUMWLWyLhjhyRaNocRMeFci3u5x5siUGOl7pZrksgODgGwM0z6Lqi717f7JMv1ntI0Pt69GifHNRgJzt27CCxdi3Wohc8plUTY3zTphE4TgcTLfGmwRG4brZu3Vro+95mHojR6IPlHrcvcr+PHp0TCtccvIMakAvjd/Xoo2u0CAhnzkH7Pqy/e/a0fJPcmvZraWsdR5+ydm7RIoLnrQOfHRxV0b3N9v559tkKgBz3XtfF+vSxOFmHDvVMfxGHSe3aYchpAU6RCrrHktkq/kb47ejRvcnBsltVqpSk7r8/DnCBhXJw164wImEkl0dRHFJ90H8911Plme3c+R3sF0vQd/XTVcs7RMCf27b9T02f/o3T97YRecqYVqz4GTvOcHLR7AMcE0jezSTm8wB+B2vJ2x97rDKRl52Qx/3aIWzvocz+DjIiE/s2+fJFB9hvM3+bEkgvCZg3X3pJ2jxOlpSAOEAExThs2EyMLz+gzF2GYuSqRjFie0KCIFu4MAD0UBgh0pVA3b4LugFiTydF+vz++/VMjl8yER4nIiBQU2SPH49nAZWs8YK+iW760ktKFyLm6FGF4cYSCSILOr1FnB8dO0azuB7FMW/V2+ymqSdJLp95pglIMN84QEaPDsZx1pZom+o+k6GnStPHH3+JEXEq97L3K0gxAE6eXA7jy2CfnY/ZUfpKwNP7J31Hd/Md7dKlK/B8N1BXrsRrCcDdSUCoriQyQjilJXePffnxRwVFWg0Mg94t8O37tf3t7h5q1KgXToXNINltW3n2vXNnS46i0aM9MyKsWBGA0TE/FCafEvl2w+ju7Ogi/969J7B4XUPU5WXyqFxVQgVoNawIh7dQVcycGQFvdzTI+e44mIo76y7bbxen0euvdwOEcR4EfqLLZLZicJg3L0iNGRPBvNEbYIclDNbd/eOpEMX40Lr1MJ6hDRqNmlA96ik//hhABF1edLmPMyQheGJiEuOdzvGXYpC7hGHwmhbFImCYL79UUJ8qzWBufy5i+BOD4pgxFqeT/X5Hv4ViqVGjMO7xJhhhUDSzcLHeP8uWbcEANhCUfvz159XVacn9eOedynDuEPu+8+ePBIE+nOhuXhguikRKLV/+G5E3f3Dd/tXAK0WK3AYoqiz6Zjld7ykX3WfbXcnJZwDbFGFejMO5mhtHV2miWpkAfVCs91BWcIDI6c6ZM03Nn9+XiKMY3RHqiYmy3gtn/Vobh/VonhH/mYCuwC0nkRCzZ4+BnugkOk0gkRkpzJkpOKcvEe2RhFE4F86HFIzhSUQEBBH1kKAleReaqBYtHL/r7C+10EgNGWLJZWgEHCEgrurVowBYLNXuJ/v+9f623j9HWYw3bfo4YJRzWlMBMQ4frjQHtpHxCQtE06a3AR7ZjJPAgQdc7wAzoJ5EPgwc+D760gJsIheh6b2hR9kPR3JQ9u2bh5w0VdTgweO4V1yE8dg35rdEvPfu3Z57ayHOp4vYVBxUstlEMAZOk0hk+jjOmenYWQyEHtn0Z/v133//gVaxCSCP/UQkxlynQbOtI843iVYaPToKsEgNxj6C5zhSeyazyjvI9nyMfDcdIEakZrbxlQT8N/v5aoRmP6YEMlACp059lfLKK11AXxyDRzTBLXpVJrVPPw1F8cuHwjPSLd2DKE0y2Qnib9OmfdB0jAIBdskt8tSRSCSBWrNmiiSsEtGhWBg4qmXZJkrfRx/lwMiTF57Rwabzw7moUE5eIzdGjCHksX23rVpFkOC3H0gni+HHfr+R31bFW6/StH79nxhK+oAwjTVyuFRtxo0LYiHaBBq2uqm2mz+yjgQ8vX+yzpll3ZFWqNCIZ+oUi0dLcsyLFy00CrZnJAi6EADqYigQGkRn6O8//lAY6R/CEPGhbXOffnd1D0li96effgtEvTF6AutABblas2YA5xnM+SSBprPucfwpkQeffBLKfJpPLVgwgvr5HFd0slXQ2jNmLCI30kZ1+PAp5uQUFtlCr5Ebo00ZaK9qsqC/269GJCdDyzSb//77BEb6DiRp9ow27NAhyW8QAe92P80J4ur+MXqyMh9KJMi8ed+Qfy1Oi451ZoASGpahQ3Or06eLYgjrjRHGs3vF6Bht28XExIP27ULy3qNaQld7e8zq1cJtbwHFNG6sGOON1mLIHzfO4gQVKlTJr+OsSF1J4jxwYLjq0aMFKPBnnFXNMttt75+33x5E5MR63olYe92UkycVVGnKK/5420OEheUE1NSZ60inZvGJBMQh/fnnK6C3WYDj4wJ0eqEY8K9hMA+EVkaoxCK5h5vjhG3E9kjDx7S9h7IK+vrbb78AnNcNCqfzRGKnuHT6bdsm0YpR1HsLJ0jPdJ23xBC+f/8+1sRfqm++mYpzOl69+upVh2tUcdJ8/71iTrGA+t57T7lcD4uzxOoYlogJWYMLXaAn9nN5t06Z8gTzPQc2WKz3z6JFi3DkvIPBP06LUhJA4vTpyiE4Re+hRo8OxWHUmnmhl94mGV4vkQvZtGltwCHbVNeul53qp7YDlblpypRQ9LWSOPeWGnqeV61aif2jNceN571wkSg7yaliOYpEPEq031dfRXKNcjKffghwo47tELz+vn37dqKHX2V+PYczmxNyU0SnnDQplHdcYRz3y9Utt9yi2YSyyjvIzem53G06QFyKx9zpZwmYDhA/C9jsPmtL4IknSqQ0bHgE9CpalQdl+XLh8C1I5MCnDpOuWruyKk2yWG/SpB9I07UgD5WmOImBRyIjPdHpk5MVyrCgYYIwTIQSjXJZ6yMiQmloYuF7/vXXnCSCC8UA8TwK1ZsY0YKtwzE/HUhADDzdu+/WlbPFQfPrm0S5e+KJCBSvWVouhus7vPxiew/pUZoEKVO+fEMWI2cd8orrHY6cT9WqESCdJmV4fgG9YzbrpZWAp/dP2h7MLb6WQMWKjUGBn9QMBLJgkwW6IB0FtS20QuXKWfJiVK7s3PFhHRPrMRZYQv80yLrJ55+u7qElSzbxjhjCfEiYpJdF5q+OHYtCgxJLNEkCuR4uaxSStpQZYgRZuDCYqLScGHueYjHc3Mx346XcHTW/iqXpySdbYkQ/ouVocVTH1baDB8UJEo0+MoV5KEIzyOl14rvq137f7t1HcG5MgV5oN/rQVfXAA5e0eS8hQak9e0JAp+bifgqDfqMBRtSn09UwaB2ryPK559phFPybe9q5rin5ZiTSa/Zsi44o+qEYd06fVnDoh5OvQAx/obwjEjDCX8H4cxVngMV5+vffkusjBN0wJ6COu0HZts02FEu275/4+CvQsHXFOXkQmqBEl8AVAQ0Jbawnycmt18zRZ65coWrixA7cR16Eujnq+CbdtnLldq7hcOjC4lgfJWj3sr0oBMk9c2ZOjIcRrH1G8s43Fs1kew/p0aPtx5FRvyXioH//9rzLfmPNF49zPlFjEBBnr9zf27YFMh/mYQ68HYT9mOt0O+k5Xnmv9+nTkWinr0G8x+qKWBFWBQHyic4zYYJjPUeiK3r0UFqUnIBEChe2OEvkfSiOFNGP3npLEX3i+mxlLfPUU9G8V9cAlGDhbaBY759PPvkYva23Eie16G3yrpbk7N4UiTqtVi0/ubV2awAQb/pKr7ZdurRgXbhQS0Dv6THnzAkhV8r/kN0iQ/OxrHF/+WUVFJKz1Y4dW3GWxtGP6M65cIw8QFTv67xTnvO5LA8fPqzpELNmnXV7z9nL5IcfAgGlliDSZ4MWjeLrd9BpHoqtW3/Tct5cuhSDnhCNHvAQa4nyRBzmtR9Ouvw2HSDpImbzIE4kYDpAnAjG3GxKQCTQpUuOlG7dWCkbKJ99FgyvaSWQee87bR0UFADaZQMczLNBM+3TFDah25AilAZQvWoJqyWy4/nn9VF/iDL3yCPh0H60ZXG/Vf3xx18oAFdQynIw+Zdi38NQaz2CMuC7kE/LiLPnf0GfrVs3BmoMwma8KEI3NnlyWZAeEMH6sFgVb0+MR+PHL1C7d8/CCGL8nL76KgjjVUXy4rBKMUuWlYCR+yfLnmwWGfjrr3cFwfy75rz2dshff62gWayD8eMdb7ty2t7VPTR06CyoAOZg2HXaXPcOcfBXrhxFIs1ZOHDX8C5dxLz5L8adaxpKVAzEkuy9evXHQR++bDpmdUvW84pjx36pDhyYowYMuOJ54/9aTJ8ezPWrDIL3A785QKyDk0ikDRv+1Bwh586dw8iQAwqWUkT33o0T7XZrtQz5HDFiDtSnX2HI1D8fi8Fv1y6lxDgmjsFVq0pAqTEOg38gz/sJDCkb1W+/baPfM5rxp0SJwhg7HlQvvPBItnsu7N8/CQmJ5CAYg/68DiRuPMmrHV/W2FiFQ0zhAHO839OtefKEEVHdHWNleU+bmvXtJDBmzBfQvn3O+ileF3peHN9t20YyZ9bHKD7YY8Op/T1kN5xM/1MSLP/442Lu+dVELB4gWjGF5/xWGAGqEIFZA6q3OzPsHMaMGco7ahzsCLEuHZL2A5S1rND7iTNEaK6sRbbPmKGI8FM4xhQ0fhYaUOt++YSBi0g3hUNSaTmBJJrWFZhwxIhQoupGoafQmYFivX8+/ng4Dp4BOPcVkQ/iuLD8GegyVZMWLfLC8DAvS+QC+fXX1VybRkT7XPToetue8DvvRHLfDoba29j1sO0rPb6L06VOnSdx2P4OMNDYEUeOzKkuX26Cs3Coz/IQbdq0kUjovugBewGqJOP0iNWAVaI37NwpkTBBvBseAADSl3fFg8YGbrCV6QAxKDizmU8kYDpAfCJGs5PsKgGU6hR7KgK95yqhjZJgcuTIISDx0iqf//xzWkuGHhX1jxYSfN99jnuWxa3QGxw7JnyRyi23pfTSqFEeFtNDcXgUd9ypuVW3BISr9uGHG7EYO68hjHQ3tKkoCnvduuEg1PoSBeLkQtvU9+SrVfH2xAEiiNNnnmmFkeCQRgHhyfGkriDL6tbNo1aunKTy5/8vvtjTTsz6mUICRu6fTDHwbDyI4cPngmqcxeKeF4eXpUePXCwkuxLq/6iXPTlv7uoe6t59LPPfIhDuztt7sufRRyNx6gODtyli8JSk8ZJ3y5+85jaHvKm/yvxRrtyboDTP60LzOhMW3ZAUNYLopukAPSJvGuoHW3mcOXMR9HFzooVjNSSz7T5PvrdsmZv8dJ3QOR/3pFm2qOvs/bNmzU4izybh4DiuqlRJxDieoEXMiEF1164QENW5uPfiVFISyroPSr58kVCsTFIFCkT5oLebt4tZs5aQ42IKCPB4LeJRryTEQd6yZQTXuRUGvff1NtPqObuHPOrErJxGAnv27Ib293mSnV/w6FpaO5J1dI0aAaxlAwDvXVMCcpg6NQgqtKsg5l07NaQPWXvNnCngM8tnyZLWnlN//vyzYoyvES2Ix8RAsd4/c+fOxQHVTqPMrlFDcl5ZQIwGukzVZMKEIJwJA9Tbb7dMtT0z/njppUexe+zhGhkfnUTl1axZiCiaXZpT33hP6dNy0aLvcbi10px8Ro8otK3PPBMF/dda6NONRSJZjy05eCTq6s8/f+DzIjYMx/lX5PlYt86Sf+Xxx+vCdjEYVpAQazd+/TQdIH4Vr9m5GwkEutlv7jYlcFNLwKjzQ4QmIcitW8eBbpyfRoY7dx4CidcGRMdewnvjUe7SVLm+4Z57LA6QNm2UZkQSJ71wPAuyRBLD/vCDAi1wvbr2pXDhZJLJWZKwpd5j/vJUAqGhISyi24HsCNM44D1tL/VnzJDk5w/63PlhZCzSJigoCE7lvjhAokkq6VkvQrPRoEE4zrgPTOeHZ6Iza5sS0CWBOnWqEt0Qrquuq0pCA/HTT8E4QB5yVc2v+3LkyAGqzTeHkMVaSkpa3E6OHKEks8xpOj98I2a3vaxduwvkfJJXzg85CNMQRnvh+F/p9pjZtcL06YuZTy975fwQ2bRufQldcm52FZOh8xKwyeLFn2BMH4tRvD3OiRfRl6sSGf0sdLNtQMaOxMj2hKG+HTWSHCCm88ORZPRvO3z4JEmjp2Lk9sz5IUeQPFhjx8aCPJ+Ag2un/oNmwponQN999923REC8T+6Ot8hX0BKZTCaCbbvmKM6EQ3Y4pIEDO7F+Mub8kA5lHT1xYgqOymhYEToRYXUHz2wQkefunR/SXqiPhI5KcopIVIZECjkqwnx16hSLGy+LoOi3b7fobhJZ5it2oZIlrwI82+vl6PzffM+ePTyHJ71yfsgo8+eXfKaXNSor/4/a+yNMnTqCOZiQQi+K+B3eeiuOtfmnXvQiEVOX1BtvPEdE0wLo7y4SNeTY+SEHkefjcTATS5Zc5LrNAXRVC/o4Fg5mMSWQzSVgOkCy+QU2Ty9jJfD005JzY0cqhVWSq9av/wGIlAs4MbDo6CzCVbxggYUDXj6FC/qFFxTIQcktoYhQsKBdpLtr1wJQHNMainQeyqxmJwGhjbj33sosRHJ57AT58cdA+OjzqxEjOtj1mrE/ixcvxP00kjDtgiwOQtM40exHJ8bHb78NhKc8ksSL3UGtZpxR1X5s5m9TAtlJAnfccRu0UYVBKHt3VnPnBuM0f5yQ95zedeRF6zJlSsNPnsOLHm40lcizIkXy3dhgfssQCWzc+Id69FGguD4ojz6apDZt2uqDnrJmFwsX/kw0pR2CxcCp3Huv5LI4Ra43whvMkkoCxYoV1PJyDB7cWo0Z8wH6S3uMotWhQCuK/lOPd21EqvpGfgilbNu2tYw0NdvYSKBXr7HkfohzSVdkUz3NV/zt6NoXVK9erdLsywobdu78AyPkS6wRHyPSsTVG4Ek4ib+B1nG+SkjorkaNeoXcSw8QHfMZaxHfRC75Sy5Hj/6Lw2EPc4V3R5CojVKlrhGpdRW6nlNEV3huoBUEvOQL6dTpxjrZdlQiSqEP9LaULl1aHTwYpFETetuXbXsxVEuUf2Yva9asImIHfiUflOrVL2I/+dEHPfm3C8kxcvbsP17nCZVR1q6drJYvX+jVgNu3b0KOk92ANhM0B6KezsR53LPnZaK1txEF0lpPE7OOKYEsLQHv3/ZZ+vTNwZsS8K8EBOFYokQgCtFx7UCiwLRuPYRFVwwGdc+PLSGlffpYlLioKIUibOFBXbxYkdjcwocq4f2igImB2yy+k8DQoW1A8zyJsSKMhKPu+5Ukq4MG5SRSojB5Xj6GeiHMfaN0riGG1l9/nQxipAbc+hEsrkK0vDNWAIgsCv76S6np0wNx1kXAMVwOh9skEOXl0nmk5uFMCdxcEhg5sguoz3CN8sHImYuzYNIkod1rbqS5z9pUqHCXWr/eNw6Q9esDCOV/wGdjMzsyJoHdu/eSN8M3xhiJcN29+5CxgWTxVkIldu5cjLrlFt+ciOS62LZtv286u0l6efDBO8gRUsJr46c4UZo1e/4mkZp/TvPUqQvw0u/BgOrdu6V8eYmK/wfdFeU1ixRxZgwb1ofcXy9DGbVGrVhxHidOPNSVFoS2AODeey8ZMNU5wG//oqf3xVhalagAHYuRDJLBqlUrAenFayhzb4dQo8Z5qIEmkAsk1nBXAhjMCRZk+fK0XRw4oIgKuzvtDg+3SHT9q682Juo/lETTlhxNHnbhsPqRI4Eg+ks63JeZNu7cuZb36VWfDEnmsz/+2OSTvvzZiTgt77/fN0eQPDWJiXHksLliqMPvv/+WiOuNqkULDBAGSqdOl9X+/cuyTOSNgVM0m5gS0CRgOkDMG8GUgJ8lUKJEsjp0yBJ3u3HjbianA/A0G0fuCJe6hEraooPz5bNwnFaqZEkId+jQVRwvpgPEl5dW0EFDh7YFQdQNNGFe0BW5Nfooq7NAjiUAnUOHFNQKITinIlCAX2YhMx4DB96qTFoEuTh4cCu4VqcRdtyUfDMPEt2RF9RWHkJjo1S/fneDKn2NRdc4UGcDM/W5ZFIRm8MyJeCxBEqVKgyPeWPQoGEeU0idPi15oMKJ7OoKTZH3VFoeD96mgTjig4LykuzaZqPBr7NmhXNeLxpsbTbzlQSuXEn0mrLJOhYxSF25kmT9eVN9nj0bC2WS7yJ1b7/9CuAMHn6zeCSBWbO6qXz5jEeBREWFARLpkqGRdh6dcCatvHTpFgzmCT4xmNepcwHAzneZ9ExTD0ucH23aNAJF/hljvqBFTAji31kpWFCiXOJU+/Z/qHr1qrHmOOSsaoZu3759NYhyz6M1HA1aqKvq1EnwmnaxfXvLWtn+GFu2hBFtA5WCD0rz5q3J/RGt7rhDHNI+6JAutm+PxLFAGEsmL+fPn/UZ7ZfYNS5cgEfMyyJOQqGTGzasL7lRu6AXD4Eadrk6f/68lz1bmgtdXdGixhwWjgZQqFAQ74Izjna53CYA25Eje3GeMYbfoRIENWLERfrwLIeSy4GZO00JZEIJEPRkFlMCpgT8KQGZUKyRq+PHzwPdE+f14VqSB23iREkgmrqr5oB9T51SoIYjmQBdaNCpm5m/PJBA9eoP4yCYBbf0RnhoF4FQ+ZtQ9CSNciw5OQAU0S2Eq1dSq1Y9jyJofFHtwZB8UlUMpe+9V0f780mHWbyTxMQk0IiHtL9Ll65g3MhFrp7iRG4VU5IXxiymBPwtgbfeeonorCs4zD/HqRqni1d5/XoFRUQE3OEdeE9ljkit9u0bq4EDR5CM1DgadPnyAKhqiqnSpYv4W+xm/24kEBUVgfHATSWdu8+dU0RH5tZZ26zmSgJZhSbF1TlkxL6CBaMxkPVXL73UC8OTZ/Qt4vwYMKApgJcHM2Lo2eqYW7duJxLZNwbzcuVSiL5eg3y6ZHoZffTRIHJYrQJg5dn8KFEhH310UjVtWhMO/41EN+BNzkTl9OljJHP2zYDWrpU8Ht5FBslIBKkva+QYHnNB20uR5NNLl4aiNyFQH5RIOh48eAJMDw1UjhyXVJUq3nUqOdQ2bgwkN07md4CEkjjVFhTozZkLi4I3a62fflpGTqABXN9jOBUToKiK14Aborv8/HMk4L4gtj2MI7EvEa33GB6q2Foc5aYz2uG1a3Kfe26/2bBhPU63OCX5bLwppUop5HSaBOq7kM+93nRltjUlkGklYDpAMu2lMQeWXSRw9GgQSmBejb9zy5Y9TMjen5kkQW/XTnJ9WJLE2fbYtatM7hdI+HiQRKUlbXeZ330kgeDgIBLxPab9SZeCvJA/X3DI+miIZjcGJbBv31GMzXN4hjbz/ATg8LhEJE+yOnkyBCqzXDhEUnBwVYTvu4EqWfJWg0cxm5kS0CeB1q3rQvt0LyHtQ1T58vFEhMTjiEud1BAmHRbIiiSp4Szu85MPqhec2YX1HSAdar344qMkL72bZNfbiZ7znB7hDGC4Pn3CSWBsotLS4XK5PUTZsg+gX6wDgOH5tbTv/I8/FCjhu+033xS/BSBx5oz3Rj2rsI4cyYGxrYD1p/npgQQefvgu5vzh0Nf0U8ePnwVEhPXNRQkODsQhGwkQqZ2S95tZvJfAv/8Kktr7fqSHYsWUOnr0qG8682Mvu3f/qRYtmooB3jPnh3VIFSsqaLJO4TzpxRw53Lo5U3wKHZSsUb0tAiCUS1m8uLc9Wdo/RPrC33+35M6ULfPnB6uqVZ9Fzw/3zQHopUqVqrAEfKj69m3P+yQF+irjXUsutxdffBVnim+oRI2PxH3LUqUeJOfbL6yd3Nd1V4N86gBePNcNLpJ9vmvXd1Rc3EYo0y7i3HB0pBjW7Aqg4nLVqtVm9fzzb6mOHXsYWsMXKVIU3VScj77Jv3XixDWYFjznxVy9eik5/y44OlmPtz37rORfWWk6QDyWnNkgq0gAbLpZTAmYEvCXBGSC3bv3mpZs8ciRU6pw4UDDoYm2YxSkX4kSkuvDdqvluySz+uCDSyR7nJN2ZzbdIs6HpKRkzQmREacoCBDT+ZERkvfdMYWPffjw2Rho24EWWqHWro1VU6bEsIhJVm+/LXl3kjAwx2jby5f/GUNJWxBp8zN9IkrfScjsKaMk8Mgj96gNG6axuOmIoaOMeuSRSBbEkeq11/LAlx4JVV0eoivKq5Yte2HEG5+pnB9WmY0b9wHP0y0YezxTO4XOq169MPIpdSABumnctcozIz8rVy4LhUSYT4awfHlujEUgOm7CIkCKqKhIJfe4L8qOHQHQuNzpi65uyj7uuacYuRUmYbRszLsmP7RYkakQyEFBgVpUr2xv0eIlon8nm86PTHqnyNpL1kmZvXz8cR8cFxcwbhsfadu2V3CgfAVdkG+Mn8ZHkrpl8eJlyCeQepuRX7GxSkUQTO+r6ylJ1Q8ftoxEHCuffhrNmnmwkaG5bPP6641YO3RT77wjEQIuqzrdKbncpkyJBnDV3WmdzLSjfPknyPn2X2iNlwOT3HHlyj3lUS9Ca/XKK08RSbZCzZnjzPlh6VLuJ8mjKrRzp09PxBHyppJ1oKflXhK6/vHHNcPX2PZ4Z88qHHFR0JyH2G7W9f3339f7xPEkBytb9hpz4a+6jmtWMiWQFSVgRoBkxatmjjnLSGDzZgX6oISSha4k+Lv1VoNakIMzljBHCeW908F6V0Kje/bcoRISElGsQx20zvqbdu06hEF6gVq37nfCmeM4zwBCb1MIJc+NgfA+wsJrgywtlfVP1DwDv0sgOfkqyPp+hMT/jjJ8xSW/veiltWunYHiOVT16fK6aN/+LBUpP0wHm96vk2wP8++9p9dtv+1igH2HRk0z+m/wstkppNGfyvs5sRcZUq9bj2p84fE+fvkhukAQMA7mzBNWeJAletOhjHIzdQJYdJ8HrZRZ6zqUsBoMffgiAOisCFF8HuOFNlLVzaaXvnjJlbsfxGw24I0YXLZuz0QkNyZo1IWrChJvTASJyeemlqurrrz+HGjXZmZh0bd+9WxGRUAA6Md84pnQdNBtWCgkJJrq6DgbH2hq9qSSVFypMMYxJPqMKFe5Cr7wz2+rVGXlJixa9VR05shPAmPejEAN34cKZmy5RjLV//vkb0Qfena+A3l577RJ5+mZjbG/tXWc+bC05NTZvnkukvHe0zxJF4otIEuupCS219CeRpQ0b5gFcMR5HdJR1t08/O3Xqog4c2M4xlrEm9+wdTyCDeusty/jy5Mnj03H5q7PKlauwLgol+kIM+caPIn6IBQtyoQPW0t2J5NJ5663a5NM5gp6sX9bifBT6ue7dV5P7oo/q1m2g7mNKRaGeK1nybtYTG4jW9qhpmspffBGKTvB6mu16NogDlGWMT0oBsEYXLuCNMYspgWwqAaYBs5gSMCXgTAJGURvW/saPDweV+4b2MzAwgEWUdY/3n9KXKHKOCpHHIAECtYWbo/1ZeduxY2dJhteFBWoXFqLLoHs5Af1LHEm8Y0FJx6lvvz0FNccKlM3uIKbbqL//PpGVT9ccezpI4IMPPsE5uQMl2LXzw3Yo+NnUqFGXMUBv5V4bb7vL/J5JJSCOg6+//gXU1zuqQYNWvDOG4zCdjgNhNjQFY+Ft7gaCuj7cwJPVuXPADjNpkYizW26JguajYJZwfljFmD9/HrVs2VgMXPXV009H4gTJiTPEwsctdZJZs/75p1B5Bapq1SJ4lz8EsnW86fywCjATfXbv3gLUbJhXhql+/XKr1q1f1wAimejU0nUoTZu+pGbNykUieO8O++mnuXGiGDOceHfk7Nla3rFCIdu4cXUiQ99hrm+pOUUkGi+7gooy+kqWK1cWI6JvAFtbt0o0VOV0PaUkkkls2bKF3ICT0CW6qw8/7AkKfbbGpS+6h33ZsmUza5Vkn0Q2VK+eCBhsif0hMvT3UyDxli0L8TonhBjST58Gru+jIlEfEixTs2YUkd1j0Aef9lHPjrsZPXo6TpDKUCyFQa3nuI79VtGDatXKg0NriN/HZ39sb35L5EK9ek2g+s7lTTc8NyFENlfzyDE1deqn5MA44JHzwzpIiQbp3z+eCOo5RD7ssG7W/dmsWVdyDnnnpJJcL7NmheGUa677uLYVRfaSN8UXRfoJCfHNu9gX4zH7MCXgawk4MZ/6+jBmf6YEsqYEZs0CWmOwLFkSwKK2KIulECbzn0HC/KW2b0/WFC+DXaZqduCAa07UMmUuo3gfSdUmq/9YuXK7evbZlqCId5K0MhZFR6EgpT4rSWz33HMKvvlYjCv7qNNGLVy4NnUl85cpgf8ksHr172rr1rUYYxM8XoiK0tyv3xUM6StxvrFiMUumlYA4Tl9+uQPvjdEkDj2iFi+O4dpdZrGheJ9ITqVkjeLsp58uEgn0LbQ8b1OHjOJm8akEhCqwTZu6PHOzidRry7u5EpQFhaCdi0LmeTE23se8+QZo1nFq3rzBJHTM59Pjm535RgJPP10OqrX/kS/J2CJ54cJAdejQbRh4avpmQFm0lwIFolT9+i/hfBcOcWNl1Sqljh27DV3nCWMdmK1MCWQCCTz77P9AfOfwyqlqPY2vvopiHZA+7xbJOSDOjkqVyqjJk1+Djrc7ybbHw58/jij9TjhDXmZuu09NmjQGZ8CNJO9//LEFJ5tvgBZ33aXUX3/tt55+pvgMCwtTzzzzombM9mZAK1YEYIzN6TOqwF9/DUDvKKWmT19O7oeXvBmarrZimJ4y5UvyWXQhf2C0mjEj2Kkj5O+/hb0hTL33XlE1YsQCdKOs59Ru1aoLwJVb1LZtusSTppJEb40fH40chqfZ52yDPFeTJ38MfWGcsyput3OZeFYvoIN+4LaufYWqVauy6QFAO8bNqoMGhaELvKdFo9v3r+f3nXfeTVSunpru60j+lVKlfJDIxf2hzBqmBDJEAsatuxkyXPOgpgTSVwKTJ0ehtJzBUOPZcQW90alTIHQER0AD9VfFi1/RojXuvz8JhUbhFFFQNCkiGZSSaA1Pi3jnJXy3UCHnLXPmvIqyneS8Qhbbs27dLmQ6EMNYHPzM+gZfpYrSHCX16n0El3MwzpOK+hqatW4aCfTqNQaDeJzTaCp3ghD6gREj4nCgjFHLl5uRIO7klRH7Dxw4xru2s+rd+zzUZWmRmLZjErTh229fJergAjzvI0i+2RAjrf4wfNu+zO/OJSAo6nr1ntL+nNcy92RmCXz0UUf0mRO8/w6Cbk3U/Q794osgkkffgrFzMG2MGwwys2w8GVunTg14L20gmvWwqlvXszBhMXh0754HPcekYfRE5mbdzCcBiRB88MF71ZIlG5l/Xc/Trka/aZOCxrQ4aPA7XFXzyb6VK3+G8uc9KFTPqxUrkhQ2f7siDo9zmvF+/PgPMbhPIz/jbKiR74G697QifYBPiuihKSmevTt8cmA3nXTpMgBH1FIcIacMJbgXGqg+faKI1nhVffPNdHQx79a0//wjciqIgX4jgCcQTOlUZJ5r0aKdlsx85szxyONLwHvJ3AfXsAdcU2fPBqvdu1MA39wCKKctxv9X2J6DqNhktW/fX+T7/Fuj4StQID80rfcTeU5SlExaQkNDcWB8jo3jBewfZ3EE6h+o5Dxp0CCKiLvp2E/0R1QsWbJYPfWUUMLqP5ajmhUqKHXixF70/qNQ6BV2VMXhNrmXRoyYAj1yZewTJz2mwpo1K0Tt2lUKqrTODvvXs7FcuSdhw1hMxBAGIi/Lxo3hROBU8rIXs7kpgcwrgfR7+2deGZgjMyXgVAJ7905PqV27k2rf/gIL/au6EOJLlyqMARJOqTRnh6POJXpjwgQFRZUibFLhZHFUy/m2r79WhIoLWsF5nUGDcsJp35EFdfqGgTsfkfE9sbGXQFc1Jzz0nJb83dOeTsCCVbt2HhYoE5UgLrNTCQ4O1BR5CbFPTobY1iy6JbB9+wGMRx/gVPMehVezZgRh3yNZ0Nyu+/iZoWJ2v3/i46+QEPFtUF2n4W/3TOKXLgm3dhhUP+/jPPWwsWeHytK1s/s9lKUvjp8HL3nG3n9/jJIEnAMHxrlMwino1gEDwjBAlcIB0uM6fZt5/wglSxwRHJ1Aih9H30wE8ez+wkmUcf/+kWrmzEFaXgr3LbJnDfP+yT7XVXJzPffcezhBYtNEd+s5S6GSq1EjivXVD1AtltHTRKtj5B768ss5IPt7EDl6UTcoS9h1WraMVh9/PF/99NNC9MVxrE10D9NpRWHYeuSR/EQi73NaJ6N2rFmzGoBQI6LyL6qCBfWPQpKfv/FGJIb0oeqJJ55iPf2IWrnyvK53o7Oj9OqVm7xVgzCyN3FWxdB2I/fPKZJ4/vXXX0QMJWLsj+JeKIPjzkIdtX37du6t4WrTpnWMN4hcnwmcdwrG+VDsBgFQtxbA6dYBx1AdtuuYLAydlXeN/vxzF2CiV7l+51SzZq4BEnL/LlgQhJ4erUaOnK4ee+xxjw7eqdNbyOIb5k+Pmjms/OGHOZH3J+rVV+s63O9q48GDB1WjRi/yjJ/mHnNPbyfvq0GDQmEIKQEAYhl5vKLRj4yt5WNiLvLue0itXXveELDWel5CxSXR2L/8skvLb2Ld7uvPfPmiTRu0r4Vq9qdbAubNp1tUZsWbUQKJiT+mnDlzEXqU4aB19kDdEQdVh3LoCJFwzyFDAuCOT1HTp0sCPvcS27hRqQ4dJJeAKK/u60sN4Ul/9lmLA8VRAnRrL6+/Hgmy/SPCGHUMxNook35KjoaCBZehTHHyOotwvP7+u1InT1pypRw8GKAOH67Itemrs4esUc2I4p01zsz/oxw+fC4LiVnwfKN9e1kmTw6Ey74Z7whCvLJQye73T/v2I0EergJ9l2Toqggi7bXXoqA5m4JBxousjoaOnjUa+fsekgXh2rW7yN+yGp7zP0HTXmAODiSysqCqWrUsRpKnWLDeljWElU1HuWbNTmhgJqEnncDhmIAjJAE0q1KymN61K4Trl4vvkRj3G2Hor6Q57a2i8Pf9Yz1OZv8UZ9KAAVOJJPwJA0o8yVBTtGhh23GLoWjDBtH/wkAEF1efftr9pqeJM+8f2zsk63+fN285yPEJau7cePQz/edDCg7WCBGqYsX25MMBheZB8fQe2rx5E8mSXyMqQSgzPTgQVQ8dEtrNfIy1G+uTXoBweEl6WY4cUbxb79fmSC+78kvzn35aSgRuS+aI88zZ7g+B/Z/zyaOaN++DIbmp1kBoxoKCpuJwNyYv6bNz55JEHm7wudPA0/vHmQTiyB7ev39n6KuXYhu4QFSD41yfAqKcNCkcIGRBQJQziLLwUSiRs4EZ3B6LF2vAgC7MWcugd4rj2idpgE9hvpBE9AKK+PXXIMCNkURs/Q9nwDhDFFDVq5fDgfC3Iaep/an98IPMsY2hyP3Yfpeu30KJ16NHa3XkyBrWHRc0e41EaNkWce7Nn69whCoVGxsEQ0UeLRr2jTfe4L5vz3nkta2u+/v777ckquxr1rTG1jtyoNGjc5DE/i3eSy4QtrpH5Lyi6QBxLhtzj/8lYDpA/C9j8whZWALiALEOX3IFjB8/D6P6PpAaYnxJ0igf/vknBETGNSaxK6pVq2QUb8cOEms/9p8SkluvnlKzZytVsqT93rS/R45UGn9o795p91m3yORarVoexjovy1NMCIK7QoWGKEmxKAnWM3T8Kc6hb75Rato0pSUDK1tWYRywKFqiMK5dK8nh70axbEKY6IOOO8liW32leGex0/bJcOvX/4A8Mdtdopb1HkhoF2bNqsC9119vk0xRLzvfP4ImrVOnJUhL4xRncpHGjZPVSz0W3g0zxTXLbIPw5z20dOlm5tUxzK+X1fnzTGx2JTAwQHNMiaN/2rSu6eYIkXnpyy9X4Rhbp/7442/yliRpRv3SpW/DAPcQTpkaoIIL2I02+/88fPikWr/+T3SPXThDYkG05mRBfg8yKeM0Os6f909WlPi+fUcxbn0FCn4dxqAUDEMpGqLz+PFAkuleY766Az3zDVCaD6RyJGXFc/XFmLPr/XP5coL2btm16xA6/xUM7bnJLVGS56kYunDmRH374npKHxMnfkNk02yiauMBcbnvVYAKbdpEAiRrorp27ee+gV0NT+6hy3h1JV/JlClHPY7etx52yZJAzvEBnJiH1fffn7duNvw5f34A74Z3/W60NDxAGh7C8yNrr7CwI0QEnIdix0IHbe1T1m/kkAekFqX2788LgG+6Rvdk3S95Hl566QkMxAfTcCmjAABAAElEQVSgEvMs2v34cQUbQjSO44U8P/dZu/TZpyf3j7ODnj17FsdYDSIP/oHqy3XEhLWPrVuVats2GmP9RJwl1aybM93n6dOniQCagWNhOfcpXg91jUiHAFWsWFH1v/9V5byboi8VNTzuRx+9i75PGW5v21CAqbNnP09+szm2mz3+vmvXTqLDPsLusBpwXAxzeRIA1hQcI0pJdLmAQyQCxLYE4ykR6q8PPxxKxJPnYDqJAqlWrTy66Vlka9uzvu+7dyvuvcJQxG2+Ho2kr6XntUwHiOcyM1v4TgKmA8R3sjR7yoYSsHWAWE8vKSlZSy5+5MhJJnCJ9MiHkj6PBf4m9eabV63VPPpct85CZ7VwoWO0h7Uzob6aPFmRaEsRmmjdmvbzs8+CMD7UIey4WdqdWWzLggVr1Jo1I0HFoC24KJK0q21bBRWEAnXh2Jkk12vzZkFdhKnw8LtRcLqilORx0Wvm3+ULxTvzn6V/RlitWgsWRIc9Cst3NhJRart2LaUWLRrjrEqm3J6d759+/SbzfH8LGsrYe9l6wYSH+rnnotX27XOyvEPZek6+/PTHPSTzbLNmI9WPP26CHihe13CjoyOYJxrz/n9RV30jlWRcI0fOZoG8GGNMIlzeCRhplMb9LgYcGC1wAASCXg6DuqIMvNAd1S23RBk51E3Txh/3T3YR3okT5+B/Pw5aNkW7jyTSycybkvrqZrf7Rxweo0fPJtptBzkxFOjuS+irV9XFi8GArXLjFEnBAP8o+m59gFiFUgsjG/1auTIOOuG2oMYvqrfeindIfwtVP0mlc+FEiMAI/AnGvxqGJODJPTRz5mdEk/ciosH1msTdQN54I1odOxaJI+WwLiePq/5eeCGae2Z5uuQ9cTUOd/skmnPLls04tz5RW7duIRIjkbwNgTj4UjAIBzGX3kekQBucu1UcvufEkC5Ogvr1j3JP6HMSCF20OAkGDpzCvfSUuyEa2u/J/ePoAEmEL9WsWZnIv33q5Zc901eF4lmcO2PHfotz/AFH3Wf7bU88UUatWnWC+8n7U129WnKH1kF3+8zrzmJiYlg7PIvT54BGdaa3w0jCyt5++x0cmj30NsGZcgWbT08onefjLI9Hd1a6qfnkIAISbdgwL0Dfb9LlPjIdILovrVnRDxKwC8rywxHMLk0JZDMJhIQEq7JlS2p/cmo7dx4C2bITY7pnSoutWB57TGnKvYRevujAdiNJz4cOVfCBWsImXTk/0A9RqMPJd+E5esB2TJnl+5YtO0CIuF5orFmjQGdb8qk8/LDzkZOnTMsDMGdOPKHi21kstcKZNBKkREHnjcw92VYCQcRhC22CL4oYP4OCAn3RldmHjyTw888bMI4Yfy9bhyG5GG+//SqoxGMgPotYN5uffpLA1atXoUnqA3JuJ0g5/QkdJUKkR4+pGFISQIn6fv47evQMdGgfkI/qDIvtK2lACEJzIAk/77nnGsaZWOaWLerpp1uAZO2Ko6S8n6RldpudJVCoUF4lf2bJ/hIQ5+qHH05TixcvQ5+NhQ9fdArb80bJUDEKIDwG/+XkjlgPRdDr6r336mTLSKBHH/0f0ZtbMUZ+gwH7E3XmzAkcBYEY91IwmAfgbL6G4zkaY3hLtWzZ6ziJwm2F5bfvs2aNJUrB9ZpEz8GbNz+PsbEEtD/n6S9GTxOHdZYtCwA1fnemd37I4CVZ9P/+V4G/2dq5XAIKHx8fr6HN9Vy/AgUKMK/+ovr27YCO8DPz/AWcGo6Bg/v3C01UGMCVW1kTz/QoJ4w2uHT8N3r0YCJijnjs/JAhFsIHOn78eSKgGmmAEUlCfrOVO+8sqfbuPaGBUbw99927g3jPlPO2Gy2XR926r+BY2M8607OFpjhOJk+eBH1vUZwSjdyO5dy5c0RFPav+gVJEHCEwqcFyoZhPFBFFrpsLMHTevGA1Zow40eani/PD9YjMvaYE/C8B0wHifxmbR8jmEpg48QtCBmNR7Lw70ZYtlerZM7UD5MwZhaFeEE6WhOpC7+QK4SBzbJs2YSCT3s02yb737fsbGhvnst27V5D3CtSDfrSDXKtXX72qbr31DGGm/2fvKuCjOL7wS0IIRHB3Ke7F5Y8WSmlLoRQoFCguLQ4FipRiwYsVK+60ULTFKe7uBCstrrGL2/97uw2xk73dTXKXzPx+kLvdmdnZ7+52Zt773ve+gzFrMTZP6U1fRJxJkQgULZofRu0HVrFkTAFx9y5BfkdFzLGpDsVxTQhEQmD4zRs/PAc1dfOuMec0uHTpnnCAvEMk8V5MnrwR7OcbVjk/okfj5xdInp4bYEwoCyNLiejDmv8yE//TTwciwuQNDC7YMVooPMe0bBmJyFBfGOgmIz/Y98IJYgEzcVogkFoRCA0NA7N9JIzYdyB9FmJW7pXtm61aRSHhrT+NGLEWDpC7kGkcZpQxb+94uri4gN3+pfSPjYj37t2FcY8N5pyouFiiJuk1hh3r+0dG+umyZuSEzUOH/g0njiuSPvtBDks25HNuhHzgWbBEVPv2soHb2Fj4GO8Rx47NBMmbX0xVsenjrkjywv+sKR4eHojCXIoIkotwbEyHs/A0nBtyovA0aSKRKNwFUVIclZmDOFH4pEmtIEFku+Yulr7aunUlErxDF0ll4SjUevVeIvJ0Fe65h8pe7LfZ++/XR+TtGThAtBOezp7NgCjiqprBWLp0CZwyXlY7P6IvzE6Q8ePHQdqsEZRG8kYfTvA3BAzZjz/+CJGiD/AMibl/fK3ou+9k8iyrY7BDhLvhtSk7PVi1YP9+dp450PvvN0BunF8gJyuilRMALA6kSARsd0ZIkXCLm0qJCBw+fBGh19rvjJmjbET95BMPLLAjkRwsAJIHhLBYDscki4Y8zJXYCLkiSfvH2CxgpkshJTw8wqTTh1n3PLHPmaPc+REbFt5gtGvnDdbwfPQxNPYp8ToVIFClSiVIop2EQTNM892eP+9ClStDr0IUm0AgODgUTk39hpI9ewi9fauepanfSFJ2Tyz3M2/eVknzXu2d+vgYYDiaRLdvrwCDOg6FWlWXLNvRrds4JL1V5vyIfRHOQbV+vQEGy2l08OBiIYcVGxzxWiAgEJAQ6NdvGuSuvCD5hPAOhYXnt3nzgmBEPwOn7woQqOxf8tbcrTuD/VWqFDZKyViuwbJevrx1+SeMDffhQ6IhQwgSnT7IQxclOTuyZZNrRhsnDxzgZOkERz6B1EaQiorbE0f7f/llRsjezIVhEx6TVFbef/99WrBgA/IqhCMayAt75oeSAbhBg2xwepQDXvEAs1F81q1bBvnsQIKvT1Pp2TMICePnQfK1e4qMCDMHTqtW7alTp4WIiPPWREZlp4GXVzrs5aqYu5zFc+yUmD59GnLX+Vusa66Ct7e35ARZvNi0g3PcuB+lyI/Yzo/oPlm+l/+NGEFwNMqOD0cIFbCTlR0hCL5CRKEDFSoULpwf0aCJv6kCAaHXkSo+ZnGTiYUAS264uUWYZWtZc+1atTwwUQ1DNMMSOD3mwXiTE5NTWrP9s8P/jz9Yo94D4bNdqEuXzxAefwp5LjaBJbMRiZkPSDJdxiZHa8aWXHWzZctEL18avzo7hljylDcIakuPHmGQFjuNMNWnarsQ7ewUgebNa+F3lk5aDGq5BXbE/fFHWrBwqmvpRrTVEYE0afSTN+NhhYU5wBErOCM6fkRGu5oyZaPinB9GO/jvIOcN2bPnvLkqis+tX78fjotHmGMtR34Y65SdIN995w9D5U/GTotjAgGBQCpGYNeuM/T48UUkiFbu/IiGi41YkycHYc2/W4pQjD4u/iYOAj4+PpQ1qzbCDO9bvvqKaMAAZmBHSSS3aOcHj5o/U05g3A3+rL/+IkQ38P6O4NCPuadduxyx38uCeWUBzn0ScyIVvuLojtKly2D9/TEwaQ4SYC27cX7wx7V//xbIeVn/24//UfM6w909gJ5xxneNhaXJOJfE0KHdQcqsjiiE8oiArQG1hZ5QpdgMmVHtEnAahxinOUdIFCnyPvZh2syaP/3kBgfSt5qj6bZv30bsBNFamHxz8OABk46Ux48fIbn8RoufB39c7NyBUpYUNcZ/+T0nYWfCLUdTnThxQutwRXuBgN0goO1JYTe3KQYqEEgcBF6+9IGMkn4/o2zZgiXpj7CwCCTzfo+OHVsCbde2SHiYmXr39oAmpCPCZAnHCSHPLJnlCj1yD0yQtXG+I5wdO6lt2z5YNE9DyO8yePxXwrA/BwyhYWCXfYWN0iokR4fL345KpUoVoOFqHOPly+WE51puh9kQ3bsHQCv2dy3diLZ2iEDWrBmQE6YCws+Nf7+U3tLGjU6IIqmK36qb0iaiXiIjkDatM5zHTmAH6nOhO3fcoAucV5/ORC9GEeCN2I4dJyXtZKMVrDjo6xtAP/2ESVJj4Q3ovHnrQUxQL0/BQ2A5LC+vW8R5REQRCAgEBAKMAD9fxo6dD7JSgGrmMktiTZliwDp/ngA1kRFgEkRoqPr1IpPV5s+XSWt161oeLO9P2BGycCGB4U5glTtCSjEzjNP/gyH6OGTQmlnuRNSwWQR4zfPixUti54UepUKFULpy5bLqrthoP3XqGMgllYXDbQicHr9DgvsO5JEe0fLlXnC2bYK82EDsd8rimTVBtbyT6gGaaThhws+I/DFNmDTTVDrF9v+LF/OBRNrbUlWL57dt2wqpPoPFekoq8Hfk/HnjZJ4FCxaYdI4o6Tu6Dkv7ccSKKAKB1IKA+lk8tSAk7lMgYAYBlteIiFDHCjXWbXh4GKI1tkDD81tENXTCgmMXcnq0hgNgLX377Tg4NbrB2dEQi5F6YIy1QRLv4fD+z6CnT1/T6dMrYfD5F/rBfjRhQhD6ICTPYo3ZMDhG/GjvXh+wGjZRnTrdwTi5YOzyNnnsgw+q4J4SGpZZ8gskFegAax/2J59EIdniGe0diR7sDoFJk/rSjBke2ISoG/qTJ4QwfA+EKfdR14FolWgIlC1bkK5e1d49S1JcvOgAJ7IODxvtw0mxPdy58wRzFOivOpVbt/7V3NPVq39TzpyBknayls6Y1du+fQDksPZq6Ua0FQgIBFIQApzrqFChIM05JTgK2tv7KWSAnqcgdGzvVooVKw5Htrok048fEwy0RBs3EmXObN29lSvHcmec6zATLV58gFas2AbyXW7rOhG1bQ4BjrTIkEG/NU++fAHYy6jbzNy/fx8OjupQnlgGkqU3HLOBcHSwTBsh5w5JktwNGxIkvwPo6NG3cAQuQv0ayCWhfZ2lxweTC9ngx4+fL8nCsTycNQUBELCVZMdebiPu38mapkbrslSeXoVzgZw7d9Zodzt2bI+T98NoJYUHr127KsnJKawuqgkE7BoBoedg1x+fGHxyI5AvXzZM/vo5QB49Ivrxx1Ak2g1FIjeiZctWwGGxBeyfUQjrLS39i33P9+49hbb4ECxUfKhJE/PjyJCB4EQJR4iwNyIePLFI6gHd0aaxu7PJ18WL54P0THawTvyJNwHR5fp1ivM++njsv6yzu2MHQRqA6J9/ZN1LTopcoQKBRUVIUCuHm7tJ/pUQOFSCETWTLnYX4nUKRyB79kw0bdpgSBJMhzPRAHkD5TfM0mwdO7rTrFnDsKH1UN5Q1EwSBNq1+4zWrr2LBH/a2PsX4C8uVKiAiPBJ5E/t5s1/EMqvXQoiepjsuGKZSi2/zbNnb1KNGvpETdasGQmWHSYjgv6JKAIBgUCqR2D//lOQmPHXBYdmzYIQ/X0REQMiKkAXQI10UrhwYTiZomD8JbPSxEaa0siRbDwmypLF2FnLx3i/8tFHISBr/Uk9e/az3EDUsHkEHMCMAMFftxIZ6YBIMusdKpxAu2PHj+Bke0VVqlgeTjpsk0eODESkyAPk0WxCGzbsowIFClhumMg1GjduCjwXwM7RD781b4t2EZYQX7jQBXu/bCCKbsc6v5DmEXIUzatX2BzqVDhK8Akz7eKVACTwCGYNK50KO368vG5TmTJldepRdCMQsF0EHG13aGJkAgHbRyBdurRIXOYKI4v2sfJEzEnQoyMaQGZAcu4QMBJeQt5qFLQtT8W5iL9/ILVpMwzJuy1P8rEb5s9PtHlzAPpdAjkt9aGysftM7Nfjx/eD1q17HDkbJrmYIkD9/TezbQm4yYkDBw8maCQTNOEJxm6CXizRokUEXVNZY5fHnyOHI7Qx/RL7VkT/NogAs55GjBgILWYPOn5c2QAPH2ZZGw84H4dQo0bvK2skaiUpApyT5dy59MSOULWFjejTp7tR//7QnxAlUREICgrBMx4ToU6FDQFaHSq3b3tBg12fMRUvTkjW+lSnuxPd6IUAGxiOHLmCZMRTQDLpDGJFG+QWawsZ0q8gOzMKUTsHoOcNi6coAgGdEbh48bpEyNGj2woVwkH2uaJHV6IPEwjwnNKw4Yf055/WGZmZgMWpGZo0MdGxwsMDBgTQ6tULJN1+hU1ENRtGwBWZqVkpideZepT79z3giChoVVeh8Ob16PEFbAnKnB+xO69ZkyCZ9QIOuS9sJnqAZeE2bToK6a6ayAuTGc4NByhmxGDMthbOpzN/flqqWzcTfpdtYBs4S0WLFo19a6pfT5kyBmodOmnv/jcKzmkYv3CuF0fHhMfj11P6nr8HjxkoUQQCqQCBNKngHsUtCgQSFYEvvmiMkOaN1KePtgmPk93VqEGY0OIOl431mzcb6IsvZkIndDLYzMWkCiNGzEOyLh9FbI24PcpOgcWLA8D4mA6D71IkTkN8qw2X6tVLYaHSANEx+yHvFSxpJTNOxpgzGzYQ/fwzJ4YktEl4UyBwEf/7/HM5KmTQIILEFic5jtJVfiXhlcURW0agRYs6MHwVwe94EhhBLyEh50+1axMigmJGDcINEsURcvG4YsOSFwnUR4ExlCumgnhlEwiwEd3L6xGMzY/BBGsCI+ZWPEODpRB+awe4bp0T2pWHFADCxkRJVAQyZHAFqzYNjM1hulyHDdseHtrmtuDgEEn+QY8BIVerMFzpAaSOfbAE0cCB02D8CED+NAMc4USZMskXYHLl9etvEEV6E9I1v9DgwR2pa9dPVDFsdRyy6CoFIfD6tS8k9vS5Ie7n1StktxUlURHo1m0Q9eq1G8ZVH8VRIL//Lic+1zowjlAuXTqELlw4D5nkalq7U9We8wX89tta7B3/BGnvPvqIkPZiOXJkx/60Fp6j3ahUKWxcRbGIADvUChcuSPfuvUWOOYvVLVa4eNERkRnWrVV//nkaFBGeIx+ixe6NVvjf/1hN4SnyaM6hb74ZYrROUh/kpOhr1/4JXO/BPrMMESrH4Oh4jrmbPU1OmO+LUK1aTbCH+xpR//hR6VRY+urEiU1wQhH2IPp06uzsjL1p+QSd8foWbp0Ex9Ue4P7kPtX2INoJBOwHgXimVvsZuBipQMBWEOjc+ROETqYnNo6qLWzInzOHk3Eb74Flm+bPNyCiYZLERHz48Lm0AO7SRb3ThZ0ALVv6QWYLoRF2UCZM6E0+PpWg05kerF6C4ZkIUbtxysqVrJErR3oYc37EqYw3BUGU4Y0JGzzu3Amg7Nkzxq8i3qciBIoWzYNcOT/DCDYOEVJFsJlzBBOYsFAmydFYv76swxwVxYyil2AY7cR3EvQtUWwCgatXH8AwMQmbhbZICDsMeZFmQB93Cz7DcGrRQo744mcEPz+UlL17HZGHKTtyKyGETJRER6B8+SKIqHTW7TrMmvPwiOXBVNFzxowZdYnw5EvzGoGjRkVJfgR4oz9u3BI868dCU/8FnNoGSRYz2vnBI2SZD5YDGT8+iPbt84NhYwXWTEPxOcIzIopAQAcE+BkVrn4ZH2cEzGzWQ78+TqfiTQIEisFS/eGHHRFNrnxuYRletQbm+AOoWtUXOcnOxT+c6O9Z2sfTcyQ1bVoJiZcn4Nl5CrkiXtKpU2+w1nqD5+htRDMthwH+ExD2GkIq7O9EH1NKuEDz5p1gpFf+XTJ1zzdvEvKJ5IXEWhZTVRIc58/0119XggSgcFGcoAf5wNChHJm0yGaiQKKH+R4kNUaPnkxbtx7Hd/Qevqv38e8OnCN74KwZrKvzg6+5YMEk+v57H4nMGj0GrX89PDywF62coBvOAcQJ0vUqLi4uyHWXT6/uRD8CAZtGQDhAbPrjEYOzBwQ4h0D37q2xSVbPNF22jMCYkXNTmLrnspBl/N//vGnduv3YrG+Bzm9AgmiR+G1Zp3Yvcq5y4r1OnYhatya0I+QsIDpzho+F0cqV2995/dko8OzZG4k5/ejRq3fH4/ebHO95Y7d8+RjkR2mNBbgHFtfMzowJa+UNxtq1BHzkCBelYwQBR9LmbdQoEpEjm5U2E/VSKAK3bz+Ck20mjOhPIYsSCUcjwfhFWDQT5JRkKTWWkDtwwBfG1e2IMuqO8+dTKBr2cVssnTR8+DxE7wyDxMQxbHQM2Iz5wQkSjE16CH7X4XTyJMFRTcjZRDBeyM8/U3eHPSFNnJiOZs/OC+nB2ZpySJi6hjieEIECBXJgTtNvWVq1aomEF7HySIUKZZB/ysXKVsarc17M8uXBPBAl2REYM2YR2KG7EN1hUMS8hR+M5s4NhJPEC1KJg6G9LSSxkv1DTAEDKFo0ryR9q8et3LlD+C6L54seWFrqY/DgMZAbKwXWu7K5AfmlsXex1Kuy8yVLRuI7c1FZZZ1qPX78iJo1q4mIl5VSguwhQ0IkKWGOauTC+6g8eeTI+m3bvGnAgEtQGGgEw/OvcgXxv0kEWrZsA4nm9PRWY/DWnDkZYIsYZvI6xk7sw+amUaOQOJHuxupZOsY5RmvVCqXDhw9Zqppiz3NOjsuXzyBanKDOQdg36HOr6cDEKBc7Aep/3bq7u4MwpF/O0gh40EuWLKnPoEUvAgEbR0C/naaN36gYnkAgMRHo2/cLMMKLIK+E9exVll+CghZyCVgeYffuIbR06WYkOjyLZHimPf9hUBDhiBIOTT1wQM55wQn4Zs4kLEwJkQ5yDgx2iERFGWAY3A/GzlAkv2qDZNB9YEwcAqmHb6X3zZr1g57mYST900eWxPJdmq7BxrEhQ9oDr9kwcjYAE9NJMmwy843zfPA9x5YsMt1T3DO8eJ82LQpa31vp/v2ncU+Kd6kGgQsX7iB8fygchk/g0AyWpCkQfQyJOCL+G7vwsZ49wxFx5AMm8WQ43uBpFCXJEGAD5KlTNxGdsQks7U5ga++BLrdBcm7AV2q0ICqefviBJcwIOYUIYfEx1ZhIhWh5OD2ckdjRA86t5njOLoT81X96ODFVxatEQoDlILp0aYrf2n9WFQ3X4cTnw4a10dCD3LR27TLIlaVP1MaBA+ngMK2peUyiA20IsN732bMH4BgNgla3dX117hwOacTHmB/wEBFFIKARgSpVKoFYYWLCsrLv8+fTU+XKCFkVJdERYFma1at3wBlQDcSLDIgENn9JXl9Y+6wx1WNaTEfh4UnngH3+/DkSXX+Ide7fWDcFJVgLGxsnR+Dv2uWNtdZw4QQxBlCsY5wHZPDgcdh3Z8B+PNYJK17u3esAB0pxyLJ9YkUrovPn/4KdQJ8o9jp1/NDfUauun5IqX79+DZEaDhIxtWJF0kXakL8b33zzrUnZzWbNmulGGioNvXV+rokiEEgNCMDsJ4pAQCBgCoHQ0D2KlyPMQm7ffhScCw+wUAyEAc1Ur/JxdlJwJAY7KDhqgZ0SSsqHH3pgsR2FCA7jixZmGvXqRTDiyc4ONtSaKkexVvn2W5Im7X79orB5kpk80fV5McY6lqtWpYf8gxvNmzcCm/+y0aeT/e/hw5fB1B6HDUgQmNqyPJGWQf3+uwNdvdoEjqJBWrpJ0rZp0jhKiyOO3gkPxy5LFFUIvHjhDXZvbzgDfQlR01YVPz9mvrnDGD8OLKgyVrVN7sr29v158uQ1nptrkLfnJJ5bUYgCM0jPuw4drEMSMtZwljhQYKALHFzOkMpygC5wbnwH6iAy7iPI4pl5cFp3qRRfW8/vEGvily/fg16/xo9KQ6lYsSjmyJ9Nbhyt6bply8EgDtxUlW8r+jqcT6J+/QwwvK+Gk14/1l50//b8V8/vjyUcOJl5tWpfQ/bDm/Lls1Tb+HmWLGrWzB2SL9PBzCxsvJI4mmQIJOX3R++b4ojr5s17w9nuL7Ho1fbPz5d69TLAALkGOYuURSWovVZStQsOLoU1rXqjsDXjVPsd4nX3xo1rQJwYj7VDMCTyAoij9dP/JwjAa0OOTh8wwAGO9ChdWOG81zl/vhtIczOsuUVVdfn+2rZtAkLcBZDuFG+H312L11nNmmXGHvcQJIcLvjue0l6o/f5E48A49+79JfYexyQnE5PylJbLlwl74BwgKv4FCSOwfKwobdo0oOnTL1OhQlY0MlH11i3OvVkT9oJdJmqk7MOrVq0EKXMIbCryPvzGDaJPPyWLzlFzqLDc3vHjJ+E8Nc7U+Oeff6hhw/q4hgUPrLmL4BxLva5YsQpzSD0LNfU7nTVrZiu+5fpdV/QkEGAERASI+B4IBHRCgDcdv/8+DUa5jtS4sQeMoWnpyZOEnXOYK0teNWzILB6SDPdKnR/cW7lyEdD5NW7oZgYzGwI9PYlGjZKZ6wlHIB/Zto2gjcnODf4XJRl34i+6+D1HRE6eHAT5qdeSXvbs2QhXsZFSv35FJBuriM0HIWm19kF98kmUZFjVU1dT+6hED1oR4NwQCxdux8LUE6HJo8ComQSt1m3YmN57J/PWt+8UaLf6W+384LFx+PfChQbq23cy8ktAP0mUREFgzZo92IR/A+mFfWBe+uNZa5C0dq11fvDgWNLm1185SbYrnLur6MaN3yCHM4f6928tnB+J8ukp6zRbtoxwQPfGb0q9JjakjLEhfAEJtEW65GsYNaoX5lN3ab5WdhcJa02blg5z88fC+ZEQmiQ9snXrMZBDglU7P3iwbIsYPNiAvGzrk3Ts4mIpD4HcubNSiRIlwJbXZgtaudIZ0mz1Uozzw14+aY5abNeuE9YjN7Ee+QkR+h9g/5ebatbMirVJFnwmBRAB0RxOkVp05Yo+d3X5sitybdTWpzMLvfz++6+IgvVS5fzgrnmd5enpjWjM7haulLpP8/do3rw1IBzWwj7Fg9hxZKnAZwLZ5zTYz+TCHv4Pq50f3H9wcBC5uVm6krLz3E9QUKCyyimwVnBwANZ3MbaZMuDCDRok7w/V3K6TkwP9+ON4yJI/NZnrg52KLVq0RAS8elINK2uUKVMW0cl11QxTtBEI2CUCxl2KdnkrYtACgeRHgCeS3r1bYkHcBEzy3WBl7MPk9RY6jQFg/kTRq1dyforGjQlsDaJcuawfc86cYVi0JPzpMgOM83vMn88RHeb73b9frrdjh5wA3Hxt+Syz4rduDYABeRMWTOmpR4/PlDRL9DozZw7G5qINVaxoPTsp/uDYcFasmANyoDyB4yd//NPivZ0hsG3bMbCbViApoAFh3gEID4+QnBUGBE9dv34CDDo3/CbdEb3xIfn53QcTM2bxau2tgqiDiAJ//O73QhqrubXNRX0LCEyYsBSsx10wFAXCQSEnlF66lOjgQQsNzZxmQmK7dn6QwvkNzq/OZmqKU0mJQLt2DZHk9R4YaXvwu7RuQ83fjcWLierUMaD9Lvw9DmnK0VS9OpJsqSxVqpQAM64BpPH24ZlhvYPz0CEHONmyIacQ2AmiJCsCa9ZsQ4SutoSvfAO8hvvhh8twikXAIaKPhFGyAiMunmwITJs2CGuHPjCY+yEpr/XDePCADaEeYAp3tb6xaKELAmmhS9WqVRvpn7EOd+zYhrXLFUQBGo/cN9bG2DE2eh886IK9Xk1jp3U/tnjxNMyj/pr6ZVL5zJn3Ydz3kpx9mjpLwY35O7RkySZEyyxHLjtPatMmAJLUIQnsBJyjbs8eB+SfyYiomtqQfv0Za2IsfFQUd3fZ2WINCdPUZdhp4+EBNlgqLe7umbCfdMbdh71DoF8/vMPbefNIkVOLGzLBgvPYR0REYT/5LYWEONCjR5HIsZMX+5VvsE+N6/CYOHESHT16hB4+fGjSUfJuQEZeZMuWDY7bZbpESxvpXhwSCNgkAiICxCY/FjEoe0cgY0Y3sMG/QFj7L/i3GBIrboigILCEiNj5MGyYOucH4xIZ6QgHCP+Ni9L06YTwa8vOjzdvCMxYOSGwtWsmJhksXx6AXBsrpUTpcUeQPO+YOZM/vxuiYvS5funSoWCDP9SnM9FLsiDg6xtAX345EovH2dgkPEWuBz+wpCLAcCE4ytg4SnBURiLniz82d8/wu1yD35RBU6gy32iXLiH4fWxJlntOyRddvXq35PxYtUp2fvC9/v47YSNgPspNCSadOoXje7BHMmQqqS/qJA0C06f3RNTFV4oT0LMues6cbAiUjdMsQfLNN2G0cuUbEBHGYB6GDomGMn58b/r33zJg5LlYFQnCzO6xY7MiV9AUYSjXgL8eTVlm5O+/n8MIp703Xm+UKuVAt279q70z0UOqRiBPnqw0YUI/SOi6Wb0GefyYo5/dEYE6GvuM/3SXUjWatnnzTZs2o8OHXeDQ1za+kycJRu+yMIonZM9xEmOWxGFHw7Nnz95FN6u9IveTKZMv9ldqe4hp17GjD+ZAbIJFMYsA72c7dOhGe/deBNFwFAiHRUHeyAzHWlZIkWVF9GIWqEfkAqGiDc2YsRuEx7WqnR88kDJlqkL22eyQFJ/kfsqUqaG4fkqrWBbad9eveyS4Lc5PyoTXAgXkiKgEFf47gI9ekshjcsXevfw7J+xN3mKv8wbSqd5wIl6HbeI7fP4VIKcXw/xKj8Xun3/uxu80P3J4YCGssDhhEZMjR07atm0H1s5YPIsiEEhFCMDPKIpAQCCQmAjkzJmZvL0dCE52TRq/0WO8f98FWvVZ6Pbth5CCkY/6g6DDurDHj0fXMv136lSigQPVO2A4zHXmzCCwU76DhND6ZGcNsOSQu7tO3g/A5uoaIWSMTH99bP4MOz+aNRuAPA7PYFCIsPibYx349esjIIdE2GQQbd4sL0LV3GiePNCVdAykN2+YyZl6mVBqsDPV5vHjV9joLYc0XaDEjIqux7mTRoyIfqf+L8uXlSgRiY3FP5CVKKK+I9HSKgRCQ8Po5s1/YfAzYNPmhM8gP+ZI6GXEKoMGtYKGck3op/9MJ0/exHM5OIHjnz8/Zsy1aEFwNMgRlrG6wFzJye4N1Lr1ZGwS52OTqDDZVuxO8JpZ/uvWTQDrfxEiyQ4h95TBqGxkdDM2TE6Z4opcJgXA/P0BBqss0afE32RCgPPLZM/uYHFOUDq8kiVDYGx8JPKAKAVM1DOJQIsW/wNTOAzPu4WIWvWXpB1NVv7vxO7dDnCceNDcuaMgufTfZsBSIzs47+RkgKOgPkaacjiazO7v0+c7zB+TINmrLqKCmf+jRmVCxKrnu08xKCiItmzZhPXrIrDPn8PI6gg5HAfMq1FwgkRhj1gaRvTBkrY/KxRYUy5evADje4A1TUzWrVEjCutseG9EUYQA52To1auf9C8cWtksgxQREYnIgCyIYtdvb1GzZiMYwNcjCl6B5paFkR8+nAnOWIT7pNJSqlQpOJOiKDSUiAk5sUuVKoSoZoITlCC/TNhvkFSPnR4c1cVkVM7byjlZCxcm/KYpwRzAa9nx4wOgvhEAgm03OEU6YQ1aF8+Dn9HfDayNI7D/5PWNg0XnJ0cMVahQEdHSv2BNpG5NHPv+xGuBgL0hIBwg9vaJifHaHQK86GSd30eP/CUGgJYb4Iny6tVIGP4+hzFmISQ5ZCkHzufB0R/OHH1ppjD7iBOfc44QLaV2bXbmeFO/fj9h8h2ipSvNbVnD388vXjiMhl59fdNIeQE0dCGaJhMCzPDt0uVHJGx8BrZUhFWjaNtW/v306sW5IdQ7K8uXj6TLl+9Ro0bvW3V9Udk4AjNnrpb09llLOna5c4eoePHYR9S/rlgxiC5c8BIOEPUQKmoZFhZO27efgMzDr/T8+SvIDDrAURgphfjfvu1ABoMTnJCNqHv3lgj3zyr1+d57eZAzayg2hz2x6ZMTyrJzgW05rLFcA4RDjuyKTjprbCCcF3TcOH8p98/OnbONVVF0jJ0gnp7f0rlzHyAv1i8wSvxL9euHIml7iMTcgy0KEQFpkHzdlV68cMX82B5O2MbShlTRBUSlREUgLCwChglYHHQqadPydxf6FqIIBHRAoHXrhpiDisNQPglGztcwXPtTrVpMyonpnNfwR45wJLYH1qkFIYXz/btnZUwt+34V7fyIimJDnn3fS+zRf/11TzjifwMb/Cr+hsc+ZfE14/D99+5wkHWT9Pq5wf79e+H074/8HAbswwIlxnjsjrjNuXPHIW9zDaS1PHC8rEaOu/diVzH7+u7dS1S5MrwuOhQmGj19+lyHnlJfF5wAO39+hA8kQqlXrz5ygbqApKme+MXDevmSQMp0hSMWD6xUWtjJ2bjxx8gluI6++CLhg4vXrJz7lf9xYUcJy2OxsgZHlMYuK1awxCbBuU3EZC/O78o5Y7ke/4RLlfKVHB+Rkb9AvcD0b5SdIWyDcsUkwuPj/KYVK1aioUOHYu1cM/YlxWuBQKpCQDhAUtXHLW42uRBo27YZHBbLsIA1PVEpGduJEyRtkNq0aUDTpq1AkvUgJD4jGFzk5OeW+jh0iPMUyIxZS3Utne/cmbXWD0NapCEm9EqWqifaeQ7755woxlgXai567ZozDG1F1DQVbZIZgY0bD8AQet9q50f0sD//XM4psWEDwXAZfdS6v9mzh9HbtwbrGonaRhHg6K59+05Dsi/hZoIbMPNfj5I3bxgM8tjBiZJoCFy58gCycxOx+fKGEyHIqAwR5+bZvHkLDDp74QRpAzZcK2nztnDhJrwOhEGQpH9qBtm4cRQcKU/AwruLHFnF1HTxrk3VqiXA0JtJHJ3EkSmXL1+HvrMvNrIu2JiWBCu7lBQVwJtPUWwHAZYmZWa0XuXNG2eqVi2h5IVe/Yt+Uh8CxYvng8FrAdb0tyDftwXPktswkoVIRjJ2sLIhq1atcvTTT61SdORRSnN+8DeZ54Nly7Yger4x5ouHyOMRhmOWv+OBSIM1YoQ75sImSKo8SmJ3e3qOxFy2Hs4UP2LngrHCfVerxv986dQpX7Dzm8CoOh/5JT4yVj3BsfDwUOSvTHBY1QEei4ODfs9eVYMQjRIgwDJIvXsPBUFkgurIJO50zJgMWKN9n+rJHr17fwf55T9hZ/GGgzoB3HEOcJQI/4tfNm4kyK0S9rFE/Ntn20bs8uIFQQKNj/DvybxNiUmBLI3Hf0eMGIlnQGdpTR27P/FaIJAaEXBMjTct7lkgkNQItGvXGMxXFxhG1V+Z2Tw//eQO43wHTJrOkNcYiMWwmyQJcvOmzIa11PvlywRGj6Vays5zP8WKhdGQITOSXT+/atWS/y0IlI3dVC2WEnv61BFMqpymqojjNooAL/BmzFiFEGGsGDWU8eMJuroJc+wo7TIkxBG/T50s80ovmkLrsbGaN/DGNgn8PNSrYH8gNgV6gWmkn23bjiEqaxikXZ7in3HnBzdzd2c9+0joH/tDS3k9Ej6OBsM+FE6Rv8CY1R7l17Wrv5Rk1MgQVR3Kly87jFn1pKiQ+fNHgmU7BI6bjxERUiTVGwJUAZrIjdzc0mGt4izlUNPjUlevpoFDr6geXYk+BALvEGBDeY0apWnRotGITFwLqdmNiPpYC0frRjhGVtGsWUNTtPPjHRAp8AVLG23efBCfZVPMaRnx1/RN8rpk1y4mrWVGtOsA7P+WSvPKrFkTYSBdh5wapp0f8XutCbL31q3eiFzsA6e9ZD2NXyXB+4wZc0DONcFhVQdYvsvJyVlVW9EocRHo2LE7cscUReSCOpPgpk1OcOiVwlroq8QdqB30zpE6XbsOhV3EPYFcq5Lh79tHiBwmELJIygcV3/mhpA9jdQxgF02cOF5Klm7svDgmEEhtCKh72qU2lMT9CgQ0IsBRCiNH9kDyc1fVId1LljhhQs1Dq1dvpdq1u9Dw4bORiDwCEhyOkNtIyBIwNmRmFRQpYuyM9ce4H75ulSpBWKSftb4DHVt0794akgCwnmksv/3mBNZFE2G80ohjcjQ/efIGNonBUiJkLdfPCuWdChUIidHV9XLnTlqMwwQlT12XqbbVpUt3YEw2rkHNcn/MiNWjPHjgAt3dxJEY0GN89tzH0aNXYXSZg4gJA4zFyu6EHSGcZ6pEiRvI5TMWn00UEoIqa2uu1v/+R3T6NMSXRUm1CNSuXZ4OHtR++69eEfn7p5XkTbX3JnoQCJhGgFna6dO7wIDsZLqSOGM3CHiAGj5v3moYOldjbqyCCPrM2B960Nq1hITHBMkqB+S9yoR9XmZEwLbA3mYf2PVDpX3JRSQS2L17OUg6/lZHwObIQYgq8qXvvuuGZxfYXhZK+fJV6coVffJNMEmPoyNFsT0E2OG6ZMlmOFbzw0lm3TPm11/TwFFbkBYu3Cj2zf99tF279qGMGT/Gb9gdBB7ln/effxJ9BR8SOz5ZGkvvwlHKvXv3gmPFR++uRX8CAbtDQDhA7O4jEwO2VwTatfsAm5gK8MK7WOUEYd1HZgT89FMkDLP/UPPmx8D8eQYWjzfCmoMRthoJxgHhOEErn8DEMI0Qs6ZZh1KPEt3PV18FIsnsdj26VN1HjRqlECqaS9JGVtsJM52WLnWjnj2hgySK3SFw7NhF5AnQR3qKNVpPnbIeAv6tenlFwgECXTpRNCPw9u0bJMY2zvwvV46wOdd8CamDCxdcNMsi6TOSlNWLwRBE/ftPgeyHAbk+rL+3774LoYCAm5B/CbS+sZEWnCfEwSEUEQDxNAWM1BWHUiYCPXq0xjzvofnmVq92hhzbZ5r7ER0IBAQCqROBukhetXHjfiQHP4U8Vr/Q69ffQ+t/AIyf45BDYA2cH1dglF4RJ2/HyJG9kRfAx2K+R1OIFipEIBV4ow/LiSCrVq0GxjgT70z1pvz43r3pqU6dj5U3EDWTFIGsWKBt2nQQ38fKMJJ7SBEI5gbw5Akh2jUDokaqSxFNnFRbFBkBdihNnboQxJ3eiN7KJO0lzUWsB4DjNWMGSXYcdn4kZvFGspcxY0Yn5iVE3wIBu0BA6HTYxcckBmnvCLx86YNQ9jtwYJTBIuMpEmU9g+ZmqJTA1Ryp659/CElhiT74gJO/RsGBEpdOwAad99+X/w0aRMgzQtS0KdGcObL2a3zcckLZiRcupUvHP2P9e05EmysXgaFNdOMGBpqMhRccLBfQokU/MKj8pXFZMxw2XPfv70qjRvWkHDnEQs4a7GylLuvwDxyoz2jYuL5tm/V97d1LYPNVFkxN66Ez2sLZOa1JJtRnsD2yVi4nwdZS+Dnm6+tKRYvm1tKNaGsEgTlzNkIP2Y/Y6KKm4LGOuSwE86WcAFKPnC8eHg5wqgTDqWJEfFnNIEUbu0KgQoUilCXLe3i+X8F6QZ1lj9dlW7Z40PHjwqBnVx++GKxAwAYRyImN2YcfNpX+mRvepUuXKHPm15r3b506hUI5YBMSIf+IHB+mk3ywUbtChRqImNstzcHmxmbuHLPgt25Nh9w2X5qrJs4lMwLsBNmwYQ9sFOuxbpsKKWgDHHO+VLZspJTPws+P6No1RxANM8KO4AFi5ijMoa0TJfIjFNpPV8Bwunr1EhKs/ytdo2DBUlSpUiVEBpdMlGvqCT/bJDhfz4cftkCU1zDYFm7BNhOEKGhZpYAjPO7cITp/Xs7nwf4jtgUlRuRH7PsKh7Fj9+5dxJJY7hxqLYpAIJUiIBwgqfSDF7ed+AhwToKdO08jVHkN+fm9glRUBHJmBEohjs+eOWFSJOhuEn2MPfTw4YSFbcyYWFpq9WpHMBWjEFoaBaNqzDlTrzgio2NHAstGTog+a1ZCJwhLkGANrWkxG3191q5lqSDW53dwCJPygKRJY134bHRfevwtXDgXWBdDsXCbTqtWGbB4U9YrJxlj50e5co3RFp4mUewSAT+/QIQdmx8666keOiQvOr28kD4OG7MMiPAvW5aQWJTl3Pi7TMSLUQUKAXEuxgvX2bPdwXZvF+e4eKMegWLFCtO5c+nQQXCCTtgoPmECIRE1mUwCmqCRkQM//5yeevVqY/MbKiNDt+lD4eERcFDtBYsV3mUNhZ32LF3FzkWeK7WWoKAo4fzQCqKdt583bzg1atSLypTxxZrMupthtmbv3m6QaBsKo5CrdY1FbYGAQEAgoBKBnTs3IM+Ct8rWMc1c8diqVSsccpCnqV69ejEnjLwaOHAckrWfghyXD3E7NWXqVFfkO+kqDK5qwEviNmy451werVu3B2nzAtbfJ2CLOA252QB8/u7YK9WCYb+W5IjgunoXlmeaP38aIkt+g7MgCtfxxTwdIUUh3b/vApuKG/J0uiKK6Vv86w4ZONs2Y5YuXYbWrPkTDqMnIEwco8OHjyPS6xmcHzdwfy/o00+JfvxRJrEGJ9zm6A2v1B87l/766yBUQz5LlP5FpwIBe0DAtp8c9oCgGKNA4D8EWFZj9+6zWDBcAUvCC6HM/yJMORTOi0iEM1M8/fMIyenBjIrVq5nF7ABDbDoYXp3hLImi7NkzQqcxEAbVt4qcH7E/hMKFSdKSbd+eaM8eimMUrl+fsLghaMDKht7Y7ax9vXUrQbdWbuXs7ADdyggsRpLPAcIjadq0OhZpP1D79pOpSxd/OITCwXAyfmccknrkCNEPP3gg9LQNNHbxIYlitwikT58WMmjGh8+OD05szhEDUB0gTgjJkVUcQYWIYLCMCJrMRI8eERb3clSTqe+N8SuwRJ0LjGofgJ2n0PNmqiNx/B0CVauWoAULmKmfcGfAbClOWN+/PyEEX520H8ucXb+eA1EGH767pnihDwKcwL58eZk5qLXHDh3k6EatDhCO9AsKckI+EXaqiZJaEeAoz1WrJkLCahSe+35UubIyJDgxaY8ebmjXCVG5CL0VJVEQePXKhy5evEc3bz6g0NAQrGEzYv38Hgg3RaVcGIlyUdGpQMDGEbh8+RT2NfoM8v33/cCwP2vRAVIMHuIvv+yLaJHZWEMbJIKQNSM4eNAR+fTy0J9/guUnit0gwM6NKmCE8T+iAUky7gMH9mI/3g/7cW8Q1cKN5H0DY41CkHv0Lb6LE0CIWYb5ex0kh4snyfi0XCRv3rzIL/ql9I/7GTiwI35Xf0gR7Dt3KsvhquX6sdsGgMVx/Phx4QCJDYp4neoQEA6QVPeRixvWG4GgoBCaMmUVjHAHsCkOw4QWBPYESYZ3zivBOvXTphHCOOVID2YuRxdmn/ftS9SyZRQ21o6YHNtQnz6fgzGwF4vGJfTRR9E1rfvLTpBvviHIbLFhNqYts2nfe4/g/ScYa2OOW/uK2fOcR4sjQLiw4TltWmf5TTL/X7dueWC3lDw9l8PYfQwOpHAYOAIhcSOHmD57RnT5clo4q1zA/nwPRvFvRdLqZP7M9Lh86dLFYTC5ifDouL3du0dg+Mvfd/7ex0+mzJFCHBnVqZMsD/f995wLhsA6ituPuXcrV6YBmy4f/fFHV3PVxDkrEShYMCfyJWWi+/f9pN9v/Ob8DOOExiNGEJ7B1jlBbt8mGjIkIyTzfhCSZfGB1eH9xYss+Qi6vA6FJenYSam1gNAIpwwmR1FSPQLvv1+Mfv11BnTMx2HN9hoEiBDIXxqHJSiIkHfNCTKb7jRpUn8YDmobryiOakLgyBHOebASDN/HVK1aJJUsGSBFaHp7O8IR7ganCM/j1UBS6EiFCuXSdC3RWCCgFYHXr18jF+NJzE2n6O3bp1hHOGN/VRGs9aowHFdFvkVHrZeI0/7Zs5eUP3+cQ6rfFCkSBRm/W4ra9+kzGIS9u3hG/olcBQbFkSDbtjli/5kHz9mdIAPaxv5Q0Q2LSkmOwIYNK0EG/RF2FF+Cr8BsYTvG+PGBkI+6R507f4S54Xfs4bCJs6OSK1chSY6ch8xETChSJWm5zRsgUQQCqRgB4QBJxR++uHXtCFy58gBshbH0+ee+mMRCIa0Rt88CBQiLYcIkLctdceTF9u1ywqvYdXnC37QpAA6QjTD05aeff94ALU4TdPa4lzD57quvCBt2InbCxE5AO3o0IUJC1s6Pbww22VmsE5yki3MtsHOFCzsUcuXKbFMSMhkyuMEg2o/Gju0JA+klhPJeA6PkHylKJVeuHMibUgEOogqUO3dW+SbE/3aPQI0alRAevQ/OxJjfDTvq+LfH0R0SkcnCXfLvcNUqgoyV/I/lTsz9RjiCa8wYV4Q0F4LG8SQ4PTlaQRQ9Eejb9yuaPn0OnmUxn2vs/idOlB0gLP/Hzl7eHJkrHPm1ZYsjNvIZ4GieBCeohd2Wuc7EOZMIPHr0GHIJmCx0KCxVrMcGcdUqdzwPRKSfDh9JiuiiVKkCkKRYTEuW7ABpZQuMi6FYGwRhDRYm6XE/e+YIAosbonod4PSoi7qdsJYCa0UUXRHw9w+EM3oWJGEvQis9QMprF/cCkXjrL+mj//HHX/TZZ2dBFGoPYkMLm1p3xh2zeJdSEbgD8f4ZM0bS7dsXQXoLhfE1QHKesgyql9dWrB8zwkmXFhHovSAf1Vc34z/LKuulOiT7Zvh3ZblwNAAndV60aBY1aTIPa14fKS+lk5PxtvfvEySeM4AYVxYEk1WULVs24xXFUYEAEDh58gStWPEj1uW+Uq4RpaDwnm716rf4nbWGXeUEfoMmGAxKO0zCeuXL10Ry9JVQIjBIJNkkvLR0KVbsEEUgkJoRcEjNNy/uXSBgCYHQ0D0wlxkvly7dlSQUli71U5yUjo1vP/9M2EjLkh6xnSB8Fdaz//xzD2zAo+AAMRi/sBVH586VZX569IjbaPlygsYmO10Ii/O458y94/EzQ561YH/4Qa65dGkaLHS/gBxWZ3NNxblEQiBNGkfJCMCbo/BwZRuaRBpKsnYbFhYOZ+NXSLToC61hlrohJKCTnR/RkUrWDHDBAv6Ncq6eCMmJ6eEht+a8IQg0QRLddJCYSyuxUbt1+8RuDTG2/v3h73XLlpzb5yZ98onJx7H0PPP0JNSRcyAVKhT30+bP7cABgrHTAxKDxWju3GH4myluJfFOFQLGvkOjRy9EFNV2SeNYVafxGpUqxZF7cmRlvFOK3l67RjR4cG46cWKZ7sxcRQMQlUwiYOz7Y7JyIp3g58y1a38jyuAuPXjw8D+yRE5Ej5bEv2LCuZ1IuPv4GPDMHggH1HOQicIVGXg5P9fgwa6UJ8//YIgeiDWsk1gDJdLnkxzdBgRUQeSnE/4l3dWVPIP4GbFw4U+IGv8ZDHQfql/ftJQwS6vOneuKaPTckOtZr4tMz6ef1kB0shcIZ9px+fVXJuUNghzyfxs5hV3eQ0j13LnjwL4/gbwgkYjy9JUIdpy/4Nat9HTmTHo8O7NTz54jMPd/ZrfrYoVwvKum5PvzrrJ48Q4BlmP68MMqICM9pyJF3h226sW2bU4goNWhlSu3WdUuOSv7+fnivithPepNDRqwFG/Sjubzz1thLwSpg2QsWbNmFjboZMQ/tV9afPlS+zdA3L9ZBEw5QHx9A5CYtTsmXG8sbM12YfTkzJkE1jizZBKenjfPEXI6kTC+Jjxn7RGW/PjlF6LFixO25FwgLF3Fcj9KFtTMdh82TDZAcYJ1ZhAxiaBhQw9I/ywS0RQJIU6SI2LhHQPz9OlrEfG0mUaPDpYSy3HkU79+MeetecWb71at0sG4UohevXoLh0qwZJxxdEwDw25ByNM1RG6fenafUNkevj/Pn78F3v3wvHxDdeqY/hT5ebZli+zYZb1+jgZhx4e/vws+Oze0LS8lPC9fXuVOy/SlU/UZY9+hX375A7IgCyHxqJ1pxnNPgwau0HyOgOEJH6iVhSO5mjd3B4N1CpyZ71nZWlRPbASMfX8S+5qi/+RHIDIyVmp4VQAAQABJREFUEr/pgYh8vo+51LrnBJpCkic9Iju/RPRIO+EASf6PU7cR2KIDhJ0fo0f3x5y2DTJtBil/nJIbPn+e16BZYWzchnVjWSVNTNYZNaovZH3XIdehySqKT4wY4YG921Jq3LiJ4jaxK7IBl5NkX7t2AXn0XsBBnB574YqI3qoMebpCsaumitdiDlP3MS9bthC2kPGQsYUHTUNp2TIzVCm2UznWS7WTMnhwV/yetyH6JQo5OZJu0M7OaRFVPwORMx2T7qJGriQcIEZAEYeSDAHhAEkyqMWF7BEBUw6Q3r09oSV+EnkDwlXdFjsOmjeXoyiqV4/bhWzsIcguWKdpH7cX+R2z4GvUkPXxS5eWo0E4dwezYXfvdoM8lBy6zXJZ/C+2VFZ0f8y24yTD7Ejp04cwacawnubOTYtcIB8gx0n/6OribxIjIBbeMYBzFEi9ej2xmH6KiCSSFpVp0sSct/YVlA7g9CuEyIFF1ja1m/r28v159OgVjGTfUbNmb2nAgFCy9LleuoTUjQNc4cRqjkTpbRBaj7A1URIFAWPfoQsX7mBeGA05EHgvNJZjx9ixVQvOTQMYp7fB/g6VnJFKuuX5q1MnN2rTphPybH2mpImok8QIGPv+JPEQxOWSAYFFi7Yi4mYNpAvhuVZR2OHdtKk7IpnnIF9IAUQMpO4oWBUQ2mQTW3SArFixCJI1niCT+UvkL2uA44jhbt1yIGL4LHLaZLSmaZy6R5AsYP36ztiLYROnobBUV506mWn//itYF3lo6Ek0jUZAzGHRSFj3t2HD8lC7eKSIhGmu5127OML7c5o5c5m5ajZ17hn0w1u1qoNIkLeIqkq6oWXOnBkqJEcoX778SXdRI1cSDhAjoIhDSYYAONyiCAQEAtYg8OzZG4T/XoQjQJ3zg6/F2qmcQ8PYpMeJ0TkUlJM3ay0sseXo6EZnzzaFIfc9GIAK0IQJpejhw1Z4PQphp+MoUyZXKZKjZUtZMmjoUEIdWVf/008JDCGiFy8IIaZyouhoDdoTJ/gYsy6QYVoUgYANIODsnAbf6YkweKeDlBxZNJJbGrIc3fWW7tx5bKmqOJ/ICOTPn52OHv0FOvDN4OTyQF4XZ7p7l4iZwNHF15ewqefnlAccYPnArJoBTfnOwvkRDVAS/q1YsSgkMZyIPxOtZdMmN/yem0J6ZBISR1ajdu3cJLlIS/1ycsmPPvLA96G7cH5YAkucFwgkIQKhoWF4hm9ApKY65wcPVZZiNWC9aiTEOQnvRVwqZSPw6NG/iJSfBked9c4PRobJZ336eCN3xgBNQNWtWxdzqrK5z9yFtm51BPO8iXB+mANJnEt0BF5DBsPFJUCz84MH2qgRQTkDRgk7Krlz50b04iTI5LnCDpN0Ay9RokSyOz+S7m7FlQQCxhHQwI013qE4KhBI6QisXbsb0RJBihmopvCoXFl2LLBUS3wJKk6cfvUqqZLXin09jibJnj0Dwh0Hxj4c5/Xjx51hNF4FFkYAFiNEN27ICWfZedIfgR2YoxPc6/btnEQ4MxKPTUcoOBqJIhCwEQSKFs1DVasWg2QOwpx0KHXrBoH5dxO/xXw69Ca60IIAJ5mfOLE38q60R/LDXTR27Cm6f/8ZnLxRkl64h4cbJBhKQNf6M0S+lZJkUbRcT7RVj4ATvPwdOjRDBMgmKWJDbU+cF+vixXSQEKkM4oAT2K+jIT25F5FAi2AADaHQ0EjJ0Zk2rTxfFi1KlCULYW7yQPLV/Igc+Q6EAkxioggEBAI2g8C2bceRSDkMjHhtQ2rYkMlEtxAd5offvWCza0NTtDaGwIIFU2noUB84DIydVXasY8cwWrXqKBz3j1QbHzkZ+Q8//EQDB/ZE1JNvgn2ZkpG8eUNw5GRCDjsw8EQRCCQjAteuXYWSBuQwdChsu3BxCYPcrb9dOfZatfoSEmDPMYeNBwqJn/QoEzwtM2f+pAPioguBgH0jIBwg9v35idEnAwInTpzHIlT7pM2RFLVrE3RUCTrIcW8kTx45R0jco9a/u3KFIBnyntmG3bs3p7x5cyIJ5SwwZf2pffsIk5tSDuWeOdMNLOwiCOceCY39zGb7FicFAsmBwN9/P6ey2uSW3w27XLkwSGlxhjp1WsnvOhIvdEMga9YMcIJ8Kf3TrVPRke4I9OvXBnPcXiQ5fq3Kmc95eAYPdiNPz/6S8+PVKx8w5n4iL69bMAKFUK1akVSsmBxRybleeH7avdsBThJnRH7UgOO/HzbF8IyIIhAQCNgUAgcPHqeWLaHRqrFwLroGDcKlObp585oaexPNBQJxEQhGdu9Dh/YgykibcZL3e507+4Fstpi+/35i3ItY8e6DD5pgjmsMIsgu5CQJtMoJwpJxXbtmxPWnYe+W04qriqoCAf0R8Pb2BkklVLeOs2RxQMQxOyo1eCp1G43yjnr1GggniD/Nnj0LEe3anjPmruru7oEciL0hF1nKZLUI6LM/ffoENp5wRKVkAqkAbCJRBAIpEAHhAEmBH6q4pcRF4P795/SeeZ+C4gFwvq7rsK3Gd4Bgza1ZvocHcfBgOiRrr25xPB99VJ1q1lxK8+dvgh7lXhiVosDMCEL0SKgkj3X3ritkv5woY8YcSDzZAQatGoJdbRFVUSG5EAgJCVOcpNLSGHktHcBZlFNpCQkJRTTa33T58j3k+/GltKDalyxZiCpXLobNi3o961QKZ6q6bY4OXLRoNHXvPpp++82QINLRHBjs/Bg71oWKFq2OaI/qMEJdRkTiZDBx/TFPRSYw/DADkCMnK1WKQgRQKOR1jkDn/Cr9+usUEQFiDmhxTiCQDAhcuXIPv299LlyhQhCIRNeRV084QPRBNPl6SZMGYesEr5aNlOvYoFWsSOTsrH1AH3wQQT17HkRH6h0gPIqpUxfSN998BQLISThCDOTubnlsDx8S9e6dEbmwhuN30spyA1FDIJDICKRJ40whIfr91pkg4+9vSORRJ073o0aNQaTzRchhHYPNRTvBNv4o2Sn0+eetsDYeFv8UBcIzumXLr1ij/wK58xeUP78jiEMOiKqMxJ7PEXu9KnDeDqYqVaoIu08C9MQBe0VAOEDs9ZMT405GBCKtToJnarBsXGVWTvxy5YojJFxiidvHr6DgPSdA37MnLU2aVFdBbYK33x16+V2QQLoT3b79iC5dukevXnnDEZMGDpp80K8tRjlyJKFQpaJRi0oCgYQIcC4QdiKyjJvWwkmUXV3Ta+3G7to/fvwKjKR1YBuelAzL5coFUObMEZAcItq50w1RcI4waOcDE/9rMHBhIYhXnj59Azm9hxQUFIqIMlc4VIsIiZJ4GKWGt9WqlaQpU4Yj2eM0yCb6w9Fu+a59fIiGD3fFJqwipK+G0JEjVxD5MRGyHwbIh1huz8YqTpReu/YLsMwH044ds6lgQcF4tYxc6qzBybOPHbuGBMM7YYTwwppMljiNjHSA8ywX9MVrIzq2mXh+6fj1CAgIMRlpbO1lsmdnmbzX1jYT9W0MgeDgUjD+2RZ7+/p1lunx1QUplhN+/hwJFTUW3pMtWrQBeUnmg7A2C44NP8yvEVJOnPhdI88yJCPT0R9/ZKLJkxeDEKdsPxi/H/FeIKA3AsWLF8Oajjdp2glmTJjx9Y0AQbM95N2OYG7RqK2o980q6G/9+vXUpEkT5Jy8g32WPpEx/Kzw8MgAssFYSNJ2TODA+OOP7bARDQWpNRDEokAqUCDuQMOR6vbo0T3YC56m8PBikM9bAcWQvHEriXcCATtEQDhA7PBDE0NOXgSiohykxLsceq+1GEBWSG/EtnrtmguMh0FItM5JzNVdZerUdPDafwojsHIJEE5MeevWI3r71o8KFcqFBOiVhcyVOvhFq2REoGzZgvj9vAZzRfsgbtxIQ+XK6aSnpX04SdLDihV/0qxZK+HcMEAuIQqG6PiXlTcsFy/egnF7Aq1dWwH1h5DBEAyD9Rb6/fe/EB0SgeSfEXi+RZKfnxOcq45Y1LtgEf4pnksfI5rMLX6n4n0KRaBJk6qYT2Yh/H4iHBivEBESgFwtCXNLvX1LtG6dMwzR6eHw6Exff/0R5iJ/6tvXU7HzIzaE1RH86Onpje/bWDpwYL4koxX7vHgtELhw4Q716zcFrEc/5HYzwLFL7wzzkZFEDx74wQn8N5y8m+mzzz6A7ExXRMHpQAdP9dDrt44OC2OGvtjO2vNXysnJAOeHG3J5OUj5vGzlXvz83kprGb3GkyYN5yuLSmCItLZ/R2wMe/bsB3La53BwzMfzaRP2alFUqlQ49nwRYG6noWvXePPogTVXH9q7twO5uYk1l7U4i/qJh0CxYsUhZxohqUw4OWm7zv37BLIC0aefPsEcPgAG+xXaOkyG1vz73L9/P/Xp0wcRz4fg0LHseHVmtg8KR3iwXF84PBbOzmnxDHCRnjFt234Jp1DfBJJ3kVjcjBkzgB4+3IF8eX44b/yG4T8hzrPVsKEPCLUX6Isv6gHbtVS9eg3jDcRRgYCdICBWjHbyQYlh2g4CzAh88MBXFxkslr9iQ1Dswnk7AgOjJG3zOXMIYc6xzyp7fRBR1qdPZ6PDh9tbbMDhljt3nqLFizfQkycvqUQJB0hfRUID0gFMBAcYoBwRMl0XxqsvwA7IYbE/UUEgkNwI1KxZFYzeq3CAwDKisRw7lh4LvtIae7GP5rwxHz16ISLADsDoFwgnhflx87Pr118DsAE/gwVxFyy8I7EpD6J9+yKMyjK8ekVwlqwBC3ELHCeDJGkj81cQZ1MKAsWL54Mk4wJs8C7CWbYOjvZ/wCSLoqxZo+AYc6C//3aAo82d2rVrhnmrGY7LDL4RI+ZIjjglkR/GsKpXjyMhX4AtuxNzWAtjVcSxVIrA7NkbaOPG32nOHAOVKZMQBCafsNxpv35hYFmH4Xu7i+rVO0ObN0/DdzdbwgbiiGIEChXKjt+8NyTuFDcxWdHLywnr1hImz4sTAgG1CLi4uCKKFQk8dEpQzOo2nMxcr8Js7FGjPGnkyEn0DOEeXl5ekBUKxtotI40fXwbOXAuLOL0GIvoRCFiJgBO8HnXqNIBzbiv2AlY2jld9zRqitm35Xxikng7RmTOn7dJIz3uwOTD8nDlzFnmHxsMm80SSqGLnRnRJB2kDV1dXypMnrxTZUb9+A7p37x7W1DfxrAqWHJ1lkQSzUKFCJp81EyYMR91t2I8ZFJNsmzaNhIP1Ddbo7ZFnbwfWTKmLGBiNv/ibMhAQDpCU8TmKu0hCBGrVeh8hgXexMQY9UGM5fpyoR4+YTjiM09MzLeQ6omj1agJzB8tuHBs4UHkkyJ49BJ1HTnZXC/JV5mkVN2/+I7FyS5Z8Q+PGBYKxHTOW6Fcs0bV16x/UosUhTHzN4ZBpb7Hf6Lbir0AgORBo27YRGHHrJMOVFmbRgwcEVk1GLPoKJMdtJPk1f/llO929e4CWLw8EW17Z5fn5dPFiBHSy/WjmzBj2tLHWLFUyaFAYdKh9wEqahqTVLZHTAQ85UVIFAnfuPIE8wT58xx7DOelEuXMHYIMWid+YE+YUV2zggujRoyeIGAqUHCAvX/rgu3UVYfewGmkoQ4YEQQprE+ba5tjswaotSqpHgJ0ff/21CdIwgYqkEploOWxYCFWt+gws00Ew2swDUSRTqsdRLQBVq5ans2fvwgGCCURjOXfOFY77khp7Ec0FAgkRKF68FP35JzvjLbOxE7aOe8TPj2CcTBxpHnaq5MmTR/oX96rinUDAdhHo0WMocrsdgvSTD9aA6sb59ClhPiZEq8tRxUOH+iISfSocIFvVdZiErcIQvrhr1x+IcF4sOTHc3KJAJHPAGjgSTo4MIJ82Bhm2At1HiAtHhLC0V3WENletWk1ycEQPtXjx4sT/lJRjx45iXb0Jdh3lzo/ofgsWJJBlvfGZdQRJ7oyUEzL6nPgrELAnBPSjIdjTXYuxCgQUIhAauifB7oy18T//vA/YrAYYbxR2ZKTa5csETVZmUMec/O03B7B20mBjHoZ8GwQmD2FjR4jEkOsac1BEt37+XK7z6BHRtGnsPMmIPB7rTEoD7NlzBguGGTAu+WMyje7F9F/OKeLpmQ7RL0Ux5slWSWvF7tVgCJL0ti9evAF2w1OJoVCgQH4YxMqADVJWdb+xr5GaXqdJ4yhhyMyR8HDtTrmUgt3gwT9BbucQIhLURYGwYb9jRzc4EgdjEVo7pcCS4D6ivz8PHjyFDmxfsOX9EU6doJrJAywZwxIknp4JJY1MNsIJfrZ17uwK7epu+PuxuarinI0jEP0dMvUMCg+PoOnT1yDR4h9wzhuw2SWjm12WPf7jDwcw7d3xu2sjyaEEBa2As0ybA4Th69HDA5u2cchBYsTLb+P4pvThWfr+6H3/LHvVt+9IRL4aFDk/4l9/yxYHtC2PpKFTpLk3/nnx3jICXl6PEFUzGDgiyZaG8hqpPz7/PCtdvbpB6kWsgTSAmYxNWQLLz68+nvlOEukrqYdi6hnk7e0N8ldl5KHy1rTf4/uBug2Mhq1AFFma1LcnrpfICJj6/iTyZVNE9+PGDYOxfw0IBjFRDkpvjCOq2rVjwidR06ZyK9671ayZCXaUm5DgNaIxrrTzRK536NBB2He+hd0jEHtNfykKNTY/h3Ph7d7tSMuWZYQMcwP68cdZmnObsPRVw4YVELX/mAoXjnuDvCe7epUgnUfE8yqTPlhWjCP98+ePW3fixPSQBhwOAu2AuCeseJc1a2YNFjQrLiSqCgSMICCocEZAEYcEAuYQyJcvO5UtWw5OAIUUaSOdsbY0clLBIBNzEpKPCGWOApPaWXJ+8BnW3p8+XY7oGDWKYKAkOCzkhfS5cwRHAnvjCQk6ib78ksAMIOjvy7INdeqEgRVxPuYCsV6dO+eFa02HlIMy5wc35XXEhAnBSCx7F9cbi00KVhlWFGbzDhgwHcndO4FRNQUJlH/FxuIYjMtHEa69HsaESXCCdEAo93wwHeQcA1Z0L6oKBOIgMH58H1qzJgOiDOIcVvxm3TonsPVKpWjnR2wwJk1ait+edc6Pkyc5+oOQRM865wdfl59tixcHwti9kv7550XsoYjXKQiBsLBwat9+NL14sQ3ONYMkdWCK6Zc2LRszoyQn3MWL6xGJtBlzmnbnB8NZo0YAGOc3UhCy4lbUIMDrlr59J0PWUJ3zg6/J31Giu3DWnVYzBNEGCJQokR8yiXkRTa0Njjlz0sEI00o4orTBKFqbQCBz5syIyC9FvN/SWlauzAxjbW+t3Yj2AoEUhcCIERPo+PHikCm17rbY+cES4ax+GO384B6YmFqhgiPyQNrmeo/XIOPHD6N587rBjvQChFV/ODgSqnxkQoBpu3aRkBT2xvmdyPdTQ4oSMYUSO2sPHfqLFiyYTTNmjKeFC+fCcXsEjuWY6LWjmHBLlvSP4/x4ge0XE9nq1iWsuWVyGktT5spFxJLs33xD0rp92zZZkYSv36dPEOSzFiMfbqSp4YjjAgGbRkA4QGz64xGDs1UEZswYDJ1GD2gYqxvhggWyk6I2iOXMnp4xQzYi8iTYtCk0p+KVOnVYhopo7lySklVxjo+VKwlyIvKkyYsAdqC0R8qP6KiUevUC6cSJhKv2oKAQMO8mQsPRYDLxVbzLx3n7zTehCHv0wuS3N85xc2+2bTtGjRr1xCR+CE4bf+j/B0p6nbVqERwqHK0SBcdOALTf/eD82Y0cAd1xP5fMdSnOCQTMIuDunp5WrZqAhMsZiHPtWFN++80JzpPcWEh+b00zu63LDseTJy/H2URYuhn2f/KiefbshAt3S22jzyOaG4n4/OEMxgNRlBSJwKBBMxGaf5MmTgyWnF5KbpJztc6dGwQygA+c5UpaWK5TqlQkJLZuW64oaqRoBI4evQrpCJltqeVGhw0LpJ9/XqOli1Tfds6c4XC6e8BAow6K0/A/XbiQHevZluo6EK0EAgoQ+PbbMZi/MsLYp6CyiSpnzkBEyzcXSF6VTdQQhwUCqRMBF7ChFi3agL1EGsgWk6L5gOWJW7QgyFASnAkJcSte3GDWWZCwRdId8fQcCULQBhBQfRNEVhgbBUeFdO0ahjXxM5BPP4ZyxuM41S6ChdatWwv67LPKkNPqCuLeOERuzEJ064+0Y8fXcF5UwhzZFvvga4i4XA2ybIxDhAmzn31GcIoQnCUEpwk7NwiR+XJkDe/xdu4kOGtkmTEm2rLaSNasHB0SYrNOpjgAiTcCASMIqFTcM9KTOCQQSEUIZMniAbbCWBjuf4Ajwl9KWK7k9tloyB72fftkpwdPNmvXEn30EUmGHs4Hwk4QU4XDEfmfksL9rF3rlaDqokVbwSTwlVgTCU4qPDB5ciCMpcupdeuGFiWrli/fCWPySkzEAdJixdwlOMqke/dw+vBDb/r660mIUhmIyRneH1EEAioQKF++CK1bN5W6dBkL1q4PmKKhZo2wHPY7Zowr+fsXxu9xHCKTYIlNBeXUqZtwOkYqzvvBkFy4IDOEOEmwlsJySJMm3aRXr3yEpr4WIG2w7f79FyDfeA5RiSFWj44d+bNmyXMjRxlxGL6WgryRkF2DxpYoqRqB9et30FdfGTRjUKoUUUDAa3r69A1092ENEMVqBIoUyU3Dh/fE57EI83QA5D2Ud8FSHYMHZ4Ss3jjMW+qjsZVfUdRMrQhUr14DOR+bwEi7HWxo6+cQdvANHZoJe8aVIlIptX6JxH2bRcDFJR2cARmoSpW31LixHNHBDg6W/eZocS78O7oEXuTGjQTDuyy7y+RQYyVt2kiKiAg3dipZj3H+jTNnNoC8arBqv8WD5jXwxIkv4STqiDX1XxQKzdhJk0YgOm0b9q0+xITSaAKsfJMwOCF3ETtujxzZBwWOs/T2bQTsKvJZJtSyigiTjNihYa5wRAjbq3btkp0j/Bm8/74BESKXYLMyY7Qy16k4JxBIRgTgVxRFICAQsBaBBw+ewSt+BBOzK5wJBCagHMlhrp9nz1g6QTbqcNKu4cPlNtu3y3k+eJLnaBBrNoHmrsf9GAzBcapwuOKqVTvgZLB+ER+7I0SFw0kRBgYDKANmyokT16VF/4YNlp0fsbthvclNmwzQvJwNCaN/Yp8SrwUCViFQrlxhLPKWUFBQM4T4eiDaIJ2UMO/ffwlGdzm/DrNg+vRxB6MoK77XvRBtNYMyZXK36jr2XPnChRsIGQ+w6hbYidtSB+Its5uaNg2FXu9lq64vKts2Ahzm/8MPPyOyT32uLNYgZglIlljTWt6+JfymM2rtRrS3cwQuXboLI4s+N1GtWhgkAO/q01kq7aVdu8Zgr/ZEYnkPGIYsg8DGnOXL08AQnRWGsGkwTOex3EjUEAhoRGD8+FmQvCsKRx0mJSsK8hYjMj8j8liNV5yk2IruRVWBQIpAwAOJB319o0B8lCMR2PHBShkNG7J8qfyPHSJbthC1aUNQiyDkzjB96y9fpodR34JV33TzRDnD9pdRo/ogksPXaudH9IAaNSLktrwP6az1IOB+AhnJjXBg+EhKGnGdH9Et5Aj9Bg0I0rI+yL/nL8mGbdpEkvzkunWWnR8xPclSWKxY0rEj5wUJhXzxrdinxWuBgN0gICJA7OajEgO1BQSYpcySHg8f3kYoogERFlFSwmBOOs6TMefo4MmawwnZofHmjZxUau9eovv3mbEm1zFFWOMICIN2cqIEVQDsmW5u/1En/gPv1q1/If8QTlmyaEezVasghEXuxyTc2GhnLLXVr98UbFYDMEkbrWL2IK9d2HjWq9dELHYWCZafWbTESXMIpE/vgvw1vSG30QWOy9N06tRFJJa7LzHCM2Rwg9ROaYQYV6V69cqTY+wsdOY6TUHnXr16rTiKLfq2WRu2bdvod9r+VqoUgoiSq+ivvraORGubQeD8+TuQWDRgvtE2pEqVCM5LIpY8UBr9aOyKV68643de3tgpccyGEOB1w6FDl/E8uIHP/KGUayxnzuxgG5aHMaSi5iixwMAgVesRYxAVKxZEXl4Pse7Dok8U1Qh06PAhnFKlYSSehDXrGxjB/KV1dMb//JUcOf3kCdGBA460erUbVa1aBaSGflh7u6q+pmgoELAGATfoMm7YsId69myN9eMtOOX9ESFsvgc5r2Nm7PsmQVKmnfnK4qxAIBUj4Ay2S7p0LIfoLZFAeW+hZX9x5UpayD5VsClE//rroEQ0i5983NpBDh7sD/LZcBAHwmFjCVHcPG1aQr4RmVA0bpzsROJj1paaNWXy244dREWLRljbXNQXCNgEAsIBYhMfgxiEPSBw+vRN6tFjHEKZDYjkiIgTajhhghzRsXs3EefnWLSIEGooO0A4sRRLW7FjxJSHPvr+mfXA+Qr0iCjkfsqUKRbdtfSX2YoVK8KapENhCYibNx+Z7GnDhgMIZTWAoWeyisUTVavyBPsWkmEXIBNWzWJ9UUEgYA4BdoS0aVNP+meuXmo7lzatsxR9Zs19sw5s3rzWtDBdN18+1plFh6KkGAT27z+F6Eh/Xe6HoyxZn1iLA+TgwXS0YoUI1dflA0mETvz8AiDvsBzSEIepfv0IaOUHQJaPJKYkR89evnyQpk5NS2XLlkRk6Df4LuRWNQpLazBrOmXjga+vtmhaa66XkuuWLJkfa+dFdP68F6KUtyEv3i1IUQZgzRwFCQ9HsF6z4vtQFdGZzTHvZEvJUIh7s1EEMiCsnp0g69atAJN6MiKKQ6Q5rgLsrBwVz0mZ790jSNI4oE4mRBwWp/XrFyOJekEbvSMxLIGA7SDwv/81RjTDCinxt5ZRvXxJ5O3tQrlzq1sjaLm2ubZbtixD5ARCwjQWLy+Ovgigvn3VdfT993L0x+3b5qNozPX+7bdE7AgpXz67uWrinEDAZhEQDhCb/WjEwGwJgQsX7oBNMBaLX3+TjFaOcmjdWv4XPXbeuHPSqPBwy84PblMNNv4lS+TkU9F9qP177Fh6GBKqxGn+5MkzTJzQ2dKh8OY/Kgo3ZqIsX/475K+CTZxVfrhbtwBowW8UDhDlkImaAgGrEChevBjdvcvLAdO/5/gdcqAMy5HoUbgfBz0tk3oMSvShCYELF67RiBGaunjXuGJFot9+e/fW6hfXrnE0ZC7VRnOrLygaWIUAJyb/5htPMP/94OgKBxM0bnOOAmrWLIS+/z4EUQDnQEDpj/pfgY0NTQwrS2SkA9Ytltdj/EziJNvnz8syiZDbhkGT4IAhSW6CNbFfvXKyOZkNK+GwuepVqpRANMhwmxuXGJBAgBHgdUqHDl3BTu8ISZndIG5sRzTIJTjrWOrRgQoXLkiVKtWBLHIHzDdFBGgCAYGAQgS+/vpbzOm/47flg0h8hY2MVFu1ygV5pXrb3J7i+vWreDYYGbAVh3jtMnUqIa+q5TWMqW4ZW87/wYoknP9DTUmD7SLLlf3zz79qmos2AoFkR0A4QJL9IxADsHUEAgKCkTNjHCRzTDs/TN0DExBYY/GLL4g4OoRZQuYKS2edO+cI9kKkxbrm+mHJkIMHnaGfHjdqIqmMjIxZcLA/WHvmRqnsHBu/bt16JElhJNX4lY1M1BIIpAwEqlUrCW1dV9yMn+Ib4jw9Dx/KSQoVNzJRkeWNChcuYOKsOGyPCLx+7UO5cukz8pw55WhKNb0xK3fECHeaPLm3muaiTSIjsGfPGRo9ejrkRC3LpbGPlBOk1qzpj5xNq+GAeANN7W5WjbBQoRx4bvnheWO8GX9fli4lRCEwu1GO3GW9a5YnZUlTdqYNGEDEBgAHBxeaMiVulK3xXsVRgYA6BMLCwpHw9x8QFJ6ASBVB2bJlQBR3Uc1ScOpGI1pFI8CSPZ9+2lz6F31M/BUICATUI1AYk3K5cg1p5cqdkCRWR9RkqfHt27PAOdlV/UASqWVoaLC0jtDSPefJ4oAyrbaVEiVkmfabN9Xv4Zo357XQZS23I9oKBJINAQ0+1mQbs7iwQCBJEfD0XI4oDj/iCUNN4YmqXz9CDgLLrbdsccLmvhKMNdhtayg//eSCUMtmyEMSV+Axf/682PzHPab2MiEhnFzLuA/1+vW/ieW89Chs9ChY0BFMgxd6dCf6EAgIBOIhwIninz1LRyxrpbSwY/LCBaW1zde7dCk9NP6FPJF5lOzrrJOTo9WyaqbukCMofXxMnTV/fOpUF+QMqEO1apUxX1GcTXIEHjx4RsOGzUBCT8vOj9iD42jb5csD6cSJXXDcHo19yuLrhg1r0a5dxtct7NBlubXHj2VmJEuZdu5MVL267AzhRKL9+xOSIRN5erLMaSByUmzH9xxfUFF0R4AN/sHBoRL5RffObbzDS5fuwgj4IzTjv8R37XtIwM1EvplZkIjzhP57T3wnv6bFi7cjPxIW4qIIBAQCAoEUgMC4cbMxt2dXtbfwA3+rV6+MiJBYgohfN5tDg20ZWstRLHc++khrL3J77of7U1uYBPf48VO1zUU7gUCyIiAcIMkKv7i4rSPAm4vt2w8jAkQdGyH6/lgGi+UUzBlxgoM5rDE9EosPoTt3ctP+/epmS77O4cNZaciQDtGXf/f3/feL0aVL6d691/KCc4yULVvIaBe+vgGQi4CGhE4lc+YoaG0H6tSb6EYgIBCIjQBHVvXp0xZSc8odr598QrRpU+xe1L0Ow6N1//401KiRxthwdZcXrRIJgffeyy/poevR/d27nAQ5PW3erHzJyk6TiRNdYDQsCUJBXz2GIfrQEYEoaDn07j0RERT+xBE+1hYQsLFWCqCxY+cjMkN55NrXX38MKdP0CZxzzBxt147ohx9ksoqlaF0meHCSYw+PY5Ds+F44Qaz9AI3Uj0D4ze7dZyExNBLRN+2QB6Yt8n58hXVmG+Rb6IbPZjH9++9LIy1TziGOnh406CcaOPB7+uyz09g3GGAQ9JPk34YODYVxLwDsZj/sFV6AzLSc6tTpjjqg8YoiEBAICATsHAEPDw9EgOwAaTQncn8qt4H88w9Rq1YZIaE1CWSX2jaJAuey0iobzBGonHNIj8IRrtyf2sIOHSenyFRJUFCLmWhnOwgo303azpjFSAQCSYbA3r3n6YMPwhBJoe2STk4E1hYn+jXdDzNVv/zyE8iGZAGrcAIiRrJCxkr5AoB7ZufHkCGZIbs1iZydE7IcS5TIB0OSM/31F2ETRbR3Lycyl3OUmB6Z8TObNqWnFi0+NHoyXbq0YO5ZN3ajHf13MCjIAZ8BLB6iCAQEAomCQLduH+NZkJOOHVPWffHisgzM2bPK6puqtXGjExKK1iR3d+XOF1N9ieO2g0DlypUg55hwDlIzwnPn0kEmqQ/0isuBFe1GLJlmqrBG8rlzRJ98gjAB+oA2bjQ+F5pqL44nDQInT96A8+AFDNvqr8eOk6+/NkCyapviTrJly4gojwbQwE77rg1LhnbtStDtJxhP3h22+IK1tEeMCEV08B18PxdarC8qmEaAvw/Vq3eBU30qdelyESQeb0T4GOBk8qczZ/whSfYESW13IM/et/Ttt1ORcyHlEWJ8fAzUtGk/OAQP0Y4dBpACOMraOGZ58hDk30Lol19eISrpB+RIOmi8ojgqEBAICATsCIGiSLDFz7MlSyqDmOUB+T/Tg/f15XwWaUFeyIk5eBXmh69MV07mM0WLFtZMCnr7liB/qM+N5MihXlo2egRRUQ42l2slemzir0DAHAL67E7NXUGcEwjYMQLnzl2BfAZ2xzoUllHYhn0660nHL7//7oSQzzxgPMgn8+TJCpmFOdS+/Ug4K15gkx0MY0H8VjHveQPPslcc+bF166QEyV6ZbcnOnIUL11NAABsMiNiAyWwEln24fVtmFXTvLss9xPRs/NVLkPAOH3YBY7yO0QolSxZA3g4TOzejLcwffPAgkooWzW2+kjgrEBAIqEbAEZaW5ct/BOt0IM2f76MoWR/LwPTqJTtTWSPf2vL0KeGZ5IHExz2sbSrq2zgCLVv+D8a8VTDOcXJY9YPl5NP79jmDEFCXOnVqjDn0GNiBq+EQ94OxOojKlAmTdJU5uvLatXT4LjlTnjwFMB/2IM5tI4ptIrBkyW/UrZtB8+DatYtAgvTdkNLqqHgj/sMP3eF4OYe13TPJATNtGrNHCREH6oYzYkQIHG5H6OzZxuI7ZyWEvDadPn0N1rs7YPAyUDETKVXY6NOxYwSS2/qDIXwMn9s1ODcnw/mU38or2mZ1jn5p3Xo4os2f4rsYoXiQLM27dasBeQYXUo4c2ah+fZ3owYpHICoKBAQCAgF9EcibNy+kMfeBNLoNEXHTKCLiJebnMHrvPX9EHZAk2Xv5shv9/bcTnptfg9DZDzYSM0YSfYenqrcGDVrAsX2dhg6F3IfKwpGvvCbWo7CMedoYHojVXQYEcB4RFRs/q68kGggE9EdAOED0x1T0aAKBypUrO/v5+Y2B3MpIVMEURuPu3Lnzo4nqJg+XKFFiGDZNU7kC/na5e/fuSpOVNZ64c+cBohzMd8K6kzduyJ50npxAXoADIiFzix0O9+7JfQWCvMaRF5xUc+fONJDpyAld6mlIrMmwyIWdIAcPLoAxcjMSf26BFEAY1asXiCRhLLtAxH2wDNWxY+npwAFnKefH0aMdEkR+PHr0SpKayJDhMSbeAKOGTWbNHj9O2IgSkiwSJENMJ2znut9950Y//tg7wbWix54jRyby83MCS08ea/RxNX+fPCHIaWU0eS01fYo2AgGBQEIEChfOBQbudESifY/nia8k/WeKgcqtS8K+zLr4LPH3229yUr2EvRo/wkymTp3caMaMIZQ5s21vXIzfgThqDgGeAypUKAsJybOYQ+FpV1nWrk0DzePa0HSWpRtbtqyLaMq6mDMfweB8G8SBW4g2ZIJABhifyyApY2k4QLKqvFrqbBYaGobomjPQgz5NV6/egdxkEKVL50ylShUEK78KjKv1MAdzRI1+5cKFOzR7tvb+MmbkNUsEIltfIzGoMmok50bbvHkaNW8+iL755rUUDas08s3YiNkgM2GCAXJeS5CTZKaxKuKYCQRmzlyH3/E2ODYDFRljeD7q2jUc69jXMHx9h/XznBRBjpkz5zc4c59Y5fyIhpR/A+w86tBhKtbxy+wymjIgoCpuRz/SVDQ24q9AQCBgnwiwNG/z5i2lf/fv38MzbhHUMY7Qy5cvQN6MgvGdiZH54BAIoVevXtm8A6Rt2w704YczqW/fYKyvlH0mBnBEWKbKy0t2fLCNiV/n1oEPeucOwaGkbBzGanEOyPLlKxo7JY4JBGweAeEAsfmPKGUMsHjx4iX9/f3XYkJTybGTcShWrFgpOD3GJxUqnNzSmIecPedbthDC8on4ddmyBPaVPEGtWEHEetL16hEMiLKRkMfLLFhOMsxJNtlpwhMPb1x4knd19Ze0fD/9tA4Y1a2JDZFcnLCz7t+/LcJAW2GTfo5OnjwP7V8vMhiCYRBywYapGJhwlWnatOpYDCR05V++fB9GxpE0bpwfnCjwXJgoPLb//Y8wBgKrjrDgIOhkEwwKcRuw84OlujJmLA+jSP24J+O9++KLD2Bk2AI5A9NsNs57ws6jR4/YmUUwXMlYxs5ftmYNJ3THgEQRCAgEEh0BZtQeO7aERo78GRIcZ/H8CMCzI5JAyHrH5GfHJktfrVnjgc1IJhhJy8Nwc1jS5C9c2PIQWaJoyBB3hKz3g/wVGz5ESYkITJ7cH9+d3lS7tp+qsH3WdV62zAPfxx4J4OHvqcz+bpzgnDigDIFIhIDOn/87LVq0GczxUMh9BkFeiAkHBKcSkzSeQoboPNYYK/EsqEHjx/eBkcFVWedmanGOMDe3SBA+zFSy4lT58uF05coDxQ4Q7jp//uxYU81D5MYgONSeax4LR488ffov/r0RDjiFn935815YR29D9Icy50fsbishZZSnpw/16DEeBKD50lo59nl7eu3nF4Doyy2I9saPTmUpVIioTRs/WrBgsxQNpbKbJG/m5GTAfqg+ruuIPYCDtA9I8kGICwoEBAI2iUAYkgTOmzcVBKvliE4PBkmTo35JciCwzeDvv73p1KlrcCqsoSxZSsE+sRh7lXiGCxu5swwZMiJfWA8QTOfDJhNgdlS8R2Ii6uXLcjXecyFIUFqnMAm2fn2zzRWd5D1ctWqKqhqttGZNJjjdexk9Jw4KBGwdAZ22H7Z+m2J8yYiAA5wWnIGUIzY4Vm43/n2Ef2qKE5wFq9DQBf/Yqh4TLqGmNwVtsmXLBGaBHNURXf3SJUJIJknSCb/8QlSgQPSZmL+cgHU37rQv7pwdIU2bym1YX7p/fzlCJKZ2GF6GSQaH7dt3wbFwFJPkpzR48FfvIkI4n8enn9aU/sW0M//q8eNXkvNj+XJfialtvrZ8lh0hnAiU76l9ewIrNCaCg502Y8akp6CgMnD8jLLYXY8eLcF22APDqD9lyBBTnRctnDx02TJevJAU0VKwoHz+wAECA1XWuGTnUUWQC3bsSA/HjzByxSAoXgkEEhcBzscxd+53kjFvxYodeGadpcePX8PIFCXJ5rm5uSIcvQScGC2pRo1SkhP30KH/wVE5XYpS+/rr/7N3FfBRHF/4RQjECVqKFilaoHhxKVIoUCjS4i1OobhrcYprcQ9Q+ENxL27Fi3txJ54QI/l/b4+DS3J32b3dJHfJzO+X3N3uzOzMu72dee9773tvJSoTfp7oC//u2WNo6VLO4eBFq1cPp0KF3v/w9ZXEa7KSAOezmjChN2hrpoDOIBiRPvKn9+wZRwi5IS/DEKwfrvIbipqyJPDypR/W+sGI8ngOA/JbKarUsCF7KPJ+pXz5COxFIrDmH4SDxDms2yOpVKn8hlUVv+f8DR4eBg8HxT3EbODpGQkjKkJiFZbMmb3giJIZ+zN4pqgs/KyrXDkCediuU+PGFVX2lvybM/XVr79OQhRQkFEnIzkSYCPQ1q3PsJbsQy4YbLJttHh778U+OQyOUOom0KZNJOjgdmJdbmEzgJCdHUcHCvBD3TcvWgsJJD8JPIfHaNu2DbDveILo1BA4ecacI6+5zLaRO3cU9pi+tG/fSUQFVoFj1XQ8B9U7TYaAZiMSxhxXeGSyM6oWpXv3/gBytmHdugVH07jOoUwr1bevLj8r5zeJXdi2tGYN539iB9nYZ+V/ZsfdvXsJjm7y2xjWZAaTO3c84ThbyfCweC8kYDMSEACIzXxVtjlQgB/1AFrMwujD8Nrr5s2bsxANYhEfBtoNRD/sLnwQf1j2KMGtZ8WLF4VB/jSMfLohb99OoGwheE2S5IWAMRgt7NlYv74O+OjWjUBBpYsW4UgRU4UNDs2bR2NxDIRn2yYoRP/CaDQBng5xIztM9aE/zsplly7jpMgPpqlRWipUYOMTOMp+I2wmeMF1hJLpDECnFTzuGkgGz/j6ZOPXL7+0wgK7HB4cbyXv8RcvCDRcOnqwPn1Mc25zVAgnJB082B7tW9tkSH988hHnhQSsXQJMJTR06E/SH4+VnyscsWasVKtWHMlql4GP/Cjqb6IHD17Cy9oOyoMdKHWipeg3Bjw6dmwOT/MSSO5qb6wbcSyZSeDbb78CRcGvWA9nI1IxSFaS6X377GjkSHeaOnUAFCy4u4miqQRev/ZHJGpPeIq/Ar1Y/NsxVrSZdqhSJR9q124YcoiNRf4My0EQZ+fUcKSI1mxOb986SJRdlnR4/foj2Q4i8fX/xReh8Ni8KgCQ+ASF88ePX0XETgBAcBmVzVTp3TsEkdZrbBoA2bXrEChlw83MUt4pjtzKk+cdcvo9gn6SS14jUUtIQEhASMDKJMCUVs2b18Q+8AmiT+XtFZjlokQJH7T7VdJV6tVrqGhW4UiusXPndjBXLETi9TsApDlK1Q5U3tFgvfBC1MU3sIt0URVh4gjj0KpV2xGtVxPOtY+wr4qQbCM8UHYyrV1b5xhqLs8HR4KsXk1Y8xRNL0bl+fPtEEEdbRHozuBJz56esIUtNakPxriY+CAkYIUSEACIFX4pyWlIMJbxPXYNrz8C/IBvv2WlYMGCRZAkcCRaR8II1wf9bbasJ2WtqlUrgWgPV+rUKRBhlpxoXEd9lS6dvH44xPDePYJnAiE8U14bBkJGjw4FyHIbxoZRoKIap3iR2bv3HECDR2Zpr+Ibzc8/E6hLGMTxwkJbD/lIvgWNCTQsBYXBkpMnL2ITcwGhkqGYD4MaOnDIXDcc4vrHHwRKgCgYU1diU5MfnM95zTUR54QEhAQSWAKmwA/9ZZmG74cfakh/TK/DUSNv34ZJtDlZsqRT/BzT9ytebVsCjRtXQaRfXlA5jsW98BrrQCB99RXFUL44xJ/zUC1d6o58T9kQfThEoimy7Zlb3+gZxOzQYTS8718C/JBnWNDPghNUr1gRiP3AKHhkLoZR4GNkzqtXflL0w6VLN6DIByAxvTOM2/lBj1eAcubMrO9Cek2Xzp04BxAeEQBCY5yy6MO1a05IqJ7LoracfNoYzaklnTGlaWAgrBiixCuBdeu2IwIJBOcqS/bshL1uMAxWjxHNk1Vlb0nT/O7dZ6q42A1HXaxYKJ0/f0cAIIZCEe+FBIQEbEYCvEfp2bMNIiGeygY/9JNLn57gOOovUVsWK1YCIDsWCBll//69NGJEDymCffDgIETGxoywePPGF3vS+Yhy9wZtVF2wYfwuRYYYds3RIrdv30LOjstw+vJHxEpqguMuKNK/wBrl9qFqOhiDNm06iOv1hLPEIeRU9cM+ieD0qrMXgfXLZGGq9eXLdYwi1aszlafJqiZPcO6PP//MhAgSR0T/PoNDWvxOMPrOGPz46Sd3OOp2h12mhP6weBUSsDkJaKB62NycxYATVwJnEEZYSg34UbVqVUcY01Zg2E4wwE1E0vOLiTUF9qKKjExHTHvFkQuLF8sHMjh51YABOqonueCH4bx++SUcxoEbAED2Gx6W9X7+fG8k90QspYrChon+/e2wOJbCayvF4Adfmg2mixcPgxG0HBZMOymqgyNj5BZe4Jcv94fBZSiMqeAiE0VIQEjAJiTAER45cmSS8jRwJEl84ImcSYWEhNKtW4+lBNhMoyOK7UggX76scASYR4MGjYaXXUUY3zMgGsQNCq47wBA30AFkhFG9GiizJoIeYLoAPxLoq12//gCU8f+Q90IZ+KEfDucu69gxEI4Jc6RDFy7chsflQHyfHUFt+TuSc/4JA8EueKJvwr5pOjwmOc9PNymHmb4Pfhbkz5+NrlzRH7H8lRXye/eiYUD+1MJO7CRubQsbx2gGxgwJ+IlxUHwwKoFz524gisjoKcUHS5cOpbNnYdWxwcLGPnv7KE2AQJ7+p5+GI9rypQ1KQgxZSEBIQEiAaMuWjXCUuQabgXzDvKHcGAQZPdqPBg7sZHjY6Ht+/o4Y0Qt50DrShg0vaeLEICmnq0Mseinus02bdwAM/ChXro2g2CqHKPcHUp8vQG0xduxgOIwWwP61AY73BLgwEM4QfcnbuyX2P1/AibYJgOnzH8bgAV7wGTOWYYxrYSOpAsA6DfKYgQzdDPjBdiSmJS9XTsdE0rIl5x370KWsN/fvM4DhBVaONcirsh85U3LTuHEuiNKOvzkzc9Srlxbz7Al2DxjERBESsGEJONrw2MXQbUACt27deqJ2mE+fPh2CPhhqvozwwTFq+1PavnfvdlgwfpPCDZlvUm7hCIYffiAslnJbxK03cWIIDAuLpYTjTk6p4lYwciQ8PALcjI+Bzhs5qfBQvXrRCHM8q7BVzOrMnRkS8habDDuMSbnRJT+YNkaNCoD38DjJMKaFITXmCMUnIQEhAWuVAAOfixdvpm3bDmOIYTCM2wNMIXryJAqb9lRQLspJyoXlBtCEmzkrV6dP3wDX7kkoP5eRMN5PyuuUO/enyKFSHN5fVVJUwmR+dpctWxB/4FVEiYx8R2FhERJ9kVYcywn3bdp+z3w/Tp++Ck4F6sBDzjVQqdIZOIXMBJXRUVBlBkkRPfy7NCyNGnFS51AkUw+gMWMm0rp1X0i5hTinS9u2TTCOKYiqVTeWrVvtoJRXgAHZMn+uPHk+wX7JD4CM4cgte8+RKIULw3VUlHglwAA2UzZpUfLlCwcwjlBrqqZFd4naBz8TORJKq8L0KPb2sax3WnUu+hESEBIQEkhgCcyfP4kWLAhQdRWmw5o+/SbdvXsXzhh5TPY1alRfABX/A/gRLAuEZmCka9cwMFI8Rt6ROtSuXU/kRZsMh1N/OnToXYyoZl2qWl/QcXF089+IGjlLxYrVl6JHOEKWS9my5XBsHSKkC2M/zPsl44XzqI4e/TEfLbNz8OfGjQm2FQIgY7yd/iiPYf16Bk684Ii69kP0xo4dJwDEjEP+spWg5QrBXipMikbkeXJhWi5OyL56tRc9euSFJPRLkJu1uO6k+C8kYMMSsExjsOEJi6HblgQQPlgMI2ZrSSQU3J+uXr0qA6fWdo6rVm2BkYZRc/n9cqKqjRvVcTTy1Rjxr149nDZvBjeIzHL16gOEb8ayRMhsG7saJx1zcXlHfn4IZ7GwXLhwBx4RlwEGWa7l1aoVDc/KR6DhQiiOKEICQgLJXgIM5I4ZsxiRAV0pQ4bNtH37azp8OBAbcX9w6PqDHi8QEQU+4O/fBXq9XxEuPx1Aq2kFIrEFtm/fORiFf4KH1QhwBq+H0nMdStYzjP0xwuhPU3DwEni4dYESNQxgzuvEHp5VXM/R0QE0Amk0SzBpFZOy4kFcuvQfZc4cAloIdYNkg23q1EFQ2PchmkeX1yU2+GF4Bc7zsGZNMJKnn4Gi3lPaT9SrVxagoBuiuQxrKnvPERezZ7vCINFcWUOD2l99VRIgjjaq0IkTaRD9YkHSNYPxpJS3FuJVRsXDxhqmH7HFcuDABfwegiUdQ4vx373rjKTAWbXoSvQhJCAkICSQqBIAwwdsDj6UK5f6y7Zq5Qej/zKTHR08+DfoqjbBCUMe+GHYEUdhFCr0HHrJMOyBfKToEBcXwxof3/PeiHOFb9vmT598sl7K/xHEFCHvy5Ytm/H8h5HJTGHqqx9/jFmhalWCbYjgWEKg/CLkLiEp16K+FoMeHCHCSdPr1CFEoxD2J5XwB76t9yVVqlRg+BiFCN0LkPtQOLOURmR2BkSZeAGc8aLvvsuBqJNGiPpdDUeuswL80AtOvNq8BEQEiM1/hcl3AiVLlkwVGBi4AjNMBS+pcTdu3DiX2LPlB76v701465IibzWmduA8Fozaqy2NG7+Fp+Tf1KJFDVldMRd3tmzaKYPZstkhWZcfjJCWTWbQoOlInh4ieW3LmoCJSl26BNO8eWuxkJcyUSOlHtaDXXbwLtfGkJNSJZky52199w8Drg0a9MUm/ClCzsNMcvSzE1WzZtHgzg1CCPsBqlbtXygZ05M0qiIiIhL5FabAsHsakStBRhW5MmVYEXkHr7FA2rXrHIzC3eDN1R3zqGqjt6D13UM2KsgEHfbp09eg3KqjxuQBjhpF8BjkV/n7DDYCtGv3DvRbzwAAstFgJvJ8DcFvZRiU+CA4OCibOiv3I0emkSJJ8uX7VFljg9pt29aFkr8VDi6BqvYod+5wTpO0cD6xgJTbYDwp5a2dnYNE9wH7i+ry8qU9ZWILEdnWHmjp0u3wdF4CKrBounSJNKEEO38+FfXpU8Dm9oL8fHBwSLr9qz6ynF/FPlr1TzLZd8CA665du2jPnnX47f5LoaFA4/H8cXd3h5G4FPavrbFGVtaE+jWxhcmRoi9fvgR19VvYMDzgiAlPzEQq//57AY5D6vcoPNzy5aORa+OY0d8zf3/Dh3cHeOAvK/Ij9vRXrEA8OjCLjRvfof/YZ41/ZqC+Z89wyPM2otabIg/Hbun+2Lz5LzhEmZ4zAys9ezKleNx+P/mEaOVKQu4RHdCxDHjPa/hTcV3eI2XOzFEmRAsXEhyxiCpWPIp1NywOVWf69F6gtWJqK1xIFCGBFCABmT/bFCAJMUWrkwDAj2EYVDEsxleAUo9OimMDkWwAAEAASURBVAHOmrUSi+hbxQvkRWQp0SpKsEgRcH9d/k/2RsrenkPqjayUFgpQl6jUTvb1DS/j7b2Xrl69izwihkcte8980Tdv3gdfdxQ2HO/jMy3rKlm20m2OtPvek6WQxKRMSsBa7h+O/Khfvw8Mm49A/Qc+DxmFFYtffolANMgLeEL1QqTIAihuHxM0y+hCkypM6dSixTB44F6FYhRmVGExvBDLvG7daAA9AYgEmQmFKgKvtQyr2NR7a7mHbEpoiTjYq1evIeeKvN+UqWGdOkWI3GBvRlM1zB/n3/SRIw9BG7EdXoUNcL//gN/6Wlq2LBjRQObb6s+yYj9hQmrQMxSh3r1/sGhvou8re/ZMAC0KwVByBgCkZVGqPJ4xY1xBCfaTqrHox5QSXgsUyI79nK/Et652vpcuueL5n//981b7PRAbBDm6kI3jzs6pNfmOd+48BYB8CdaJYFCn6Lx41eZEYX73yEgPypkTVi8bLHoQIqmHbi3jSGo5iOsbl8CmTf/D+jNIsg00bRqI/A8E4ENX18fnNdbH/2CU3oc8We6o94cEhBjvyXqORiD5xI4d2xFhPRs5te7BcM7POjsk845ChJo9aJqKwUmgDwzoFTV5/pma+c2b53Et89EQptrGPp4zJyF36BOj4929ezf23cEWJRF/+JAAXBOiJkg2+GE4ttatw+nUqauQ9XJEjvwMG8lVw9Nx3qdJQ1SzZpzDMQ7ky8cOITEOGf3Adqkr8NAtw15YoggJpGAJCAAkBX/51jz1vHnzfonxce6Pd0lFfcXyef36ubTAZcnCn+SXZ88ISrX8+uZqsmdkZGQE0Hxo2TIKKz/372v3037wIAoRJRlkX18/xIcPXyKcch599pllmwR9P/pXNq7ly2dH168/gNKMTkV5LwEGp3TeHvgvpCIkoFAC1nX/jB69BEn2nskGPwwnW6NGND18+AZ0WDPg8cTLR+KWSZNWUcaM12jwYGUKHDvYrVsXDK/B+eAWzo+1I0fiDlz11azrHlI9nWTaAefiMkXTIHfKv/9OoHVjb225LeLWGz06BMDfKmrXri4iQJohB0xq5DpbiX4DpVwicVt8PMLGh969XbEXKANKhwFwTrFXvDf52Jvu3bRpfalKlY7wlPS3iB7szz8dMIcC+P1WUD2W2GNLrp9r1aoMg9tN7OWUPStjyyMUrIdnz3J+uXyQPZ/VZg/05k0AQLntoDg5SM+e+cDAqcvVwY66efJkwfr0DWhdq+P3BOuUwuLrGwiwbCroWYIREaWjJxk/no11ZNH9p7/8rFnOiCr80WbvQbk6jn6+Wr4agh5JOQ4t5yT60lYCTFPUvXsbgKEnkKg7AFFncfvnvRw7/H39tR/9958f1reWoGptgmjJKdJaFbdF0h85efIk9sztsfYGIJ9EoGS7YJ1SXziv0D///E2LFp2j8eOzgnZyldm8Gvp2lryGhYVgLbWkZdw2PAemWjT2e964cRGoMwPjNpJxZMoUGKegXqhh+Bg/PpBq1x6LNaRVvPRXPCRj95qMocap8sUXAXCovYRoQ3iUiiIkkIIloJ2VNAULUUxdWwkULlzYCd4IK9Ar358Tbt68eVbbK8jvrWTJd9ICqjRJ4UeDtPxrmavJil1kpDzvxNy5syAhZJSUWFEtz3Ig9gfR0anB951a9vX18+jXbxp16BCMjZP+iPrXTz+NAl/+G3h651TfWTLpQReuz7vVaMXfUTIRgZiGCglY0/3z4MELeJbvAdes5Uaxtm0jQWlzFon7bgJMgFtUIpVbtx4jx8dW0CLAImdB8fTkBIWBeGaOhkfwAqNeaxZ0myhNrOkeSpQJ2+hFmJ6Dk1paWv77T0f78MUXlvaga8fJr8uVC8dv/RSivb4CHURDqlq1JGgeJtG4cU9BaxeEfCHRiKTSAS3MY81RtVu2uME71QV1esB4oFPg5e6LzI3Yy8sDRp0hiN4aA1AlSJFX6JYt9vDkz4Tf/SCx/poTcqxzzZvXwD2wgnr0CFMFym3aZI/ErZVgNHOSDF1q7weO4ps8eRWtXbsd90MozZ0bCUrFj/QjrAvcuRMA+pIHWF+WwhDWAfVqyn5eh4aGw1N3EaKeAiRKEhYLU6j89hsDe4R+dUa7WOKK9+ORIwSD66f47VSzqftQTx+jRMeJVxgWVOA1jEEQNpaqvYcsuLxoYuUSeAcUoG3bxliXzlKvXhwNFv+A2flvyxZ/gJ1/0oABb2nixLnxN0rkGvPmTcU6PJuWLvWHU4Hxi7OzQ/ny/OeHfFl+iNqsAaBkLtWq9Y3xBiqOenhkIh8fFR0YNA0P5+e2o9HfM0fDWrKP8ffXJQWfPt3gQha85f1+lSphtHXr1nhba0ETqb9I2rTv4Nj7xqhM9HXEq5BASpAAsFFRhASsSwIAP0ZiRKxinwP11aikHF2+fLoQSVb8lZTsoIHmcHQtCufKCg2NkN2VA3YrZcoUBA2M7CYmK27caI9EvVVMnjd14unTN+CkvC15cyoFj0z1ycfZE4U9PkUREhASSH4SWLToLwAAb03m/JAzY3489OgRhHxB6+RU16zO7Nne4F4PVOW9xk5ZmTP7QMm8qtm4REdCAnoJFC1aBN5/qfQfFb8eO0YweihuZrRB7doh2KOc+nAuf/7sABFmI3JrOkCaH2GELoJ8XxlB2ZUBSTrzgCaiPn7XIwFsrvgAfnxorMGbKlWK0cyZI6l587RIGuogOZCY65aBpD59XGjVqjzIZzKLvLzec6CYayTOfZCAm5szKFUawSjo/OGY0jfMdT57thuMi22UNjVa38cnEJ7b3ZD3bzMdOBAEb+9ICaQwNHby+vL55wT++FDcrwGIEJkPGpORxNSNpsqTJ6+R42kx9uVtAPq0AFC+B6BJTIcmpjjJk4cQPchOR6Z6Mn6ccw4OGpQWv53hYn9sXEQSsHEe3H2zZ08F4NoYv/Nq1KpVbXwvA/AdboeOZZnjgonLicPJTAIMFOTIcQEgpTzwQz99fl5MmRKE6K7tMHZv0h+2itelS+fR0aOzkIPLNPgRe6AVKnDibV88t7vRsWNHY59W/fmLL0ohp4o2a+n16wRnyfxxxhQlGSXCLdIz2KGzWjV1EbD6AdWvHwhnp78offr0+kNGX9nuoVUJDraDjiKTa1Sri4p+hASsUALCkmiFX4oYErV4L4OSAEPCPv/88+jYfzifk+vAY2eZ/lyBAgVy8TEtC3snMWfiWYUxKF+CwIt5srUo//5LiMAIh9J0UHZ3v/zSkmbNclOsSBlegBN8LV3qAoNkI8PDst5v23aCGjUKwYZROyCIL3zvnj0otT6RNQZRSUhASMC2JLB9+xE8N9Tv9qtXJyhnlySjR2JIgI1ff/99FjQ+6q/Wrl0Qnrv/U9+R6EFIIJYEKlQoAoXbcn4JNrQWLRqrUws/cj8XL96I0/rzz7PBmNsG+50p2Hetwj5qNQCGuaDe+IUqVfpC8tKO00ijA9z/4cOLcM0K4G13AyDiKEWwMtgBinR68YJAZ0IAZFwABHmCdqm1lMw9XTptDDYaTcNmuunVqzn9++8n8EBWrory/rRLF1d4IncFaOyles7BwaGIRuqNvDSP0GeoLCCb6W4WLgzBnvRf0Ln9Fme94XWBgY/69bsg8e0mGA5fwgAfBIrHaIn6KvagmQaLbXOtW+vutdjnY39moGT1aoJB3wug3USxN44toPef9+zZCQ79EjR1ahPy9ByHfDEHYZS+iEic04jiWUQnTnRFboOCeMYME0CICRmm5MOPHj3EejQfz4Vgi8TAIMj06QHIBzKQgtij0QoKmDVoxYrJ2GsGyHrWGQ45Y0ZOvO0H4LkDnBX8DU+pfl+6dBmswY6qbBf6QezdmwbP2ribcgZAHBzs9NUUvWqZ35X3QJcu/QvqTWQpN1N4TVATuWvY9fXrnqA4K2J4SLwXEkiRElC+60yRYhKTTmQJMDEjr6rm/vQ+Um/19cLDw2O6VGkw6JcvnaBU6BafJ0/kd1igAIejE715I7+NqZocEt+37zsoUn+gP2jiMkrp0vmRCLEYNilAcCwskyenQVLQuvCAy6C4h/PnLxLThzFHJoehMm+y2hIZyRzJ0Tab4FHt/EV7IYHkLAF//2AAvRGyEyGbkwWH7OfObU937yIZUyKUq1cfIJzezqKEiLGHx7rQ+fO3Yh8G9d9rcMYfBuXKXPC8j4Mn4hQY37bDiHw3juEtTmNxQEgAEsib91Pcoxno2jXLxOHrSzDkWtY2dit2evTx0WBjELtjlZ85kmP+/CG0f/8SzLUDDMxlsA/6RIpE6dgxBzxmayIypR9duOANA3wj4XGvQt4crbx+/SQ463yCfBvyjV4MRP34oyu+hybUtCnccQ0KG7eYjvD48StINHuNXr70Mzhr+u3QoXORw+UFIp6VqREcHdK/fxilSnUNc9jx4QIcTVKrVnfsf7fhXgoCQBIl7YfZoYmdqowVNpROnkygsSKMg2jMGJ0eETsi5C20ni1biL79FhzBExwBai5ELpVcxrpM0cfeQlDdurWk5cu74bu5D5DIFzRG0ZL82TmLI3kaNGAZBkGGPvgOFyICqBSej1dTtNzE5GNKYOnS2cgX4Y+k4DGPK/nEORy++y6Y/ve/tUqaJVjdoUO70u+/+1lMP8gsFz//7EvTpv2m6RjTYYNRqFBJ1QwWbHfYuNEZa/ePccbnCM/WsDCmu4tzKt4DbNPJnDnearIqsH0kJCQEEYTtEEHqZbINO18cP27ytOwTPN8zZ+yQZB7IiyhCAilcApZbR1O44MT0E04Ct27divfpjKiP+xhBTvC1dgPV0vKEGs2lS2ngcRgO5YVozhzeKMu7EisyLVsSFGmioUPltTFWi5MicvQJ802+fh1CS5ZsgdcF3MNklClTekvh/DlzvqKqVZWt9OvWOWCh/JR2724j40pxqzx48FQCjvgMe0Vv3EhY5OPWU3Jkzx7CPEomqAeokvGIukICQgLaSeDRo1eIGIM1SaPy2WeRoCF8Lhl9NerSZDdXr96H0sZYvPqCdEswqkZAQQsHIORE587dQs6DBQBAHsIIGw6gJQwh80RsBLt+/SCNGuVKT586w6v1Bzxj64jno/qvIFn3MGhQJ+xJxmJNDsZ9pmyqzEXNnvdaFO4ndWrrVUEyZPCE0es76U+L+Yo+jEsgfXoP2rNnNnjyZyCx+Hnsb4NBFfUx54ZhK85Jx3vTpUvdAA70gEGxonSa8zYcPHgRe3RvULzdA5WUPX3ySRR4zu2Qr4PIz88eYEFFREo0MRolceHCHQBaJ+FkBEuTBYVBkPHjQ0DNtoIaN64Kj2onjK0voqef4PO7GD2+fKmjuopxMNYHNsozJdamTUyzRnj265Lg8u+P+fHZI5ipaKZOJdCIOQOoc4/Vg/jIlFYtW9aDHK/hNxyGddG8TNi4zfRGNWs+wvfWgBYs2Iy19gvzjcTZZC8Bfrbs3PkXDRyIH53K0rr1W2rffiHsCR1V9qSuOWwsAGbvg45PXT+tW0cgUnILZDMO4JAKdCjWMHr3Ho3f7FnYH/wUR6fou5o2zRkgckuAzh76QzFes2bNAodKH0X5vrgDYPZSNGiMziz8wIAE/30JypBMQMh82cPESOF1j21AaiPMT5wgyp+/OGTiaeQq4pCQQMqSgPVqHynrexCztVIJ3L1rL4UetgApV716BI8yTt4pb7A//0zwUNN5chWxIOKQF0ZOisjJEXnRbdEiEmPYJRsASZvWDV5i0yRF7Pp1H+rcOSJegwcbJX7/PQ1Al0+hfE2GR5Rlj4ioqOgP12rblsdO4Nxlg4c82cWuxRyYc+a4gdcfnYgiJCAkkOwkwJ67Sg2y5oTAfbHymhjl7dswRK4gRE2j4uxsh6i5UBj5loBa628aOzbIqFGwRo134KkPAAASgOf2IiTu3Qnj4Cgk7QVCIoqQgBEJVK/+Jeg8SmE9PUG//qrM4Mse02DOkO5FI10rOnTjBivj2RW1EZWTpwQ8PFwRzTYU0TWXEQ2yEhEV90EvFo37g6MCo+nVKycAG2kAZtjRDz/Uhrd+E4DAOsMWA+ddu7IB7jH2uEGgE4kLnugiJnYgWuQQjGJ1QLHWLsbedt68tUhsHKRq/eGkts2bhyInzG569uwlwOrnccAP/vZ4Ly+H053tiexExX+8jHG+E/YETpuWYnhtJ9ISZ3M33vDhvZBk+BoiQJQhtqyrLVvmA+/2psjxctqkAdXmBCIGbJEEnj59AjCVVEV/6C+cNSvByP0av+MIPH+AZiooDOht3rwRFIx/0o0bt/BMiJCeC05OaQDUFQXNXls4PNbE8wUPmHjKtm0bEGUmLzLOXFdpwKZZtWoEnttHEO1W21xVRecKFCiI+bRHtPN8gLzB8YKXsTs/epSpKrMAuBoe+9SHz9WrN0ROlttwHEKoiIKSOzdJoPrXXytoZKLqw4cEho0s0tlFixYjGq0+wHrj3wsD+RwFwsC3JYXXnHHj0gLkH2pJc9FGSCDZSQAmAlGEBBJOAojU2Im/i4Z/BlfrYnic3+fPn/9Tg/NJ/rZFi29AgeAgKS2zZ+sACThPyCps7J81i6CUET16JKvJh0qs1AwbRlIUBXuCcWEFK23aSChXb3QHZPxn+qpDhxbAQFYN3mluMHzYSZ7DsZsyv+TSpQ7YQLnjGt/B82IWrmd5oqwsWTJIXmt8nWzZCJsZXWh/7OvK/Tx3rh2VKlUeG73P5DYR9YQEhARsSAJstGeKO63K48eOlCVL4gABbm4u5O+vTKE1N8/g4GgYbiYiPH43lDjjRj3D9p9i1ZwxIwRrzR2A5L/Sw4dwMxZFSMCEBKZP7wujxWfIR5BKMqKYqBbnMIOKe/fG40odp5XxA6dOOVLp0l8aPymOpkgJcA6WDRsmI5pjOfJgDMdetDtAiS7gLO+F6OspyBeyBvvinz+AH2fP3sTzrju8qm/CaB0kOScZ8/RnMOGHH6Jx7wbhmbqN6tbtSYGBIZKM38EydPz4JQAW6kXeokUE9IWtAK0PUM+e4UY7zJVLZ0AzetLEQZ4T8+7zc97F5WMlTifg7AwrpCgxJHDixHHQoO0GwKsM/NB3whTGHTr4wFjYT39IvKZQCdy6dRuJtLVzbvnsMwfYA+QbBNiJZ+XKxQDzCgP4GIjnylHo9C/o5EkfOGT6AKR7CpB0N147o04ROnBgf7zf1MWLR0FRrc1eu1SpQNCwnoz3mkor9Oo1BEDR16D/dlUUdbprlx0NHpwF+U22IMm5k8nLtmjxM3l7u0gU3SYrGTnB+V3PnDFywoJD3E/x4l9JLQsXLoK59jUJuDIu0rEjA2gWXAhNZs5MjUiTBvjeS1rWgWglJJDMJCAAkGT2hVrhdAphTMVi/emHmTnW8WJIam56xdK3SsTXbt2aYJH0gGGOKF8+HaUVUzkx/66coldY6tYlbEzktNCFuHfoQFDQ4lJu5c0bgn4uyuvofS1X1zQIn+yDTdRMcNvWQYh3Oqpe3R0Kpic2Tp5ILOoOz4OMMOA1ph075mPzENM7TtHF3lf+8sti2BR9fLz06kV0+jRBlsp727ZNRz/WuLEGLhfKL2/VLa5cuY/vdj21ajUUAFc3fI89sYmaBq7jA4hcsj5+dasWphhckkqAKWf8/R00CS9nAPnmzXdQXBPOw5wjVvQRJgzMXr2qjSGK8yUFB0fA2HUNBhjmlpf/tdSsGQ1D4Rt4Ig+CYgd3YVGEBIxIgCl6/vprCvKUlYNXvCvdvm2kksEh5r0eNMgZBuScqOuB36nBSQve8u9zwwZnXLu6Ba1Fk+QuAXa+qVKlGAw+9RDh9h0iiKsDBMkRw7v59u0n8NIfjogLf+xp5Rnz2B42dGgYIjMe4Bk5GFRSUVgnHiPSxE5V9If++2Bu+NevfTHuIJP5oDj/B9PaalE4IW+xYnm16CpJ+nB0DMCzRPt9/axZI7F2+qv6Ttu1i4Chcz+ij14liWzERa1DAhER4aBhUk9/pZ8NO0aGh8sD5oKxGWzZsi5oUEcjj5AP2CCC8XsnGPb1vZGUM698eTZwB9Cffz4HS0IHRE50l55tH2vFfPfw4SPQBMY8Zuknjoh48ADhnBoXe3hbzJixFPvgLtBt00o5QXjfYKqwjaZHD3fQhJfA3uIAIivgeWmmpAePbP36LZHDRBl1FwMgHL3KVIRqi7d3OuQogUHpffn111/B+tEbwH9a/aEYr/woYlYRpbllFy9ODfkVgPPApBj9iQ9CAilZAo4pefJi7gkvAXBN5kqIqyRUv7HHyorYpEm9wR08CYuqbvOxdSuBr5igeOmiO6pVozjKztOnuvNr15LEYbljhy56ZMUKQl9EvGGJ7anG3MCrVxOSQnLScwIfctw6Xl6RNHLkbBgh7mKT8xM2QvKtY/nyZUWCxZ7SFNk4/vy5L8ZghwiNDPAiw65Mw/LNN+VAS7AennlAcVDYiMdzY/CIKTQGD44/pJjD/WfM4FBWDkmPBvXXaGwGm0MhbqrhSG2zqxMnrmIzMwsGAR94QAZBrtFSmDbL7Pbtm1DcDsMQ6gSgqww2zV2xoXKzzYmKUacoCVSr9iWizw5KEWNqJs5ga8GCn+G57KCmmxhtOQn5smVb4Z18Snp2Io8iqEyiQdGSRjJCXbkSSWApsJizWH+xY8f4uR9Jw4db5nVYsSLBm/kN1q0V6KODvlvxKiQQQwIMgjDt0J49Z5BnYBH2Er5YL0KoaNF3SMjJyTkJDhN28DJ1pStXnJDwuymMLA0QNbIB6/Ja7EPkGXFiXPT9hw0bHOCJWBS818YVfWNtxDEhAb0EOGqjU6cxoEcJsMiQ17p1JADrh6CB+x+irLNT7tzYOGlUMmaMRLSy6c7YYMgR12yw4+hoNWXtWlfsieup6SLJ2hqCH1FRCHHRqDxBwhQfn3smE83LvQxHu/34YxD0sZWgyYFCJkqKlIAnqBfevMFmT6PChvO0abHAxlPCkcW7Vatv4dR2DUBveDy1daf5ebJhgz/0682gEQzH83Gh0XaGFNVGKyg4yIxbUVHgV0qAwiBI377DELHXGI5+I7CfPQ9gPAL77SDkPdLlI7t+3Yn++ccNwLMH1oT+cKr4UbJryBlOnz4jkBtqD+gW74HCSx7Ixc+F1q0J+yDCHkjOVYzXYR0lKiorcgcWjlGBo0AqV66M3E4/ARz2l/70FZjG6u5doq++IgA3Olr22HYkfV1+ZcBkwAAPOGqVgFOkN3QTbZy0DK8h3gsJ2KoEtNt12KoExLiFBMxIIDx8t+RzsGTJNoRULofBIPiD0sLeV0uX6iIb3N11IerYs0h0V7w4M1/2qVNelCFDBCIrgiSqiZOIFOU2ly6RZLDOkIG9QYju3dOFtjdrxqH6hDBI44MaMoSwOBKoABjRzwxO0KlWmwCxTp3u8La7g7DLj3OJhE1v1CjCuAlKhS4vCMvKsLByyMkfWU61a+vAJvaaYR7ndu1cwOHcGhudRoZNUsx7VvwHD56DDd9RmjIlCEZe01NnWa9Z40B//OEOKraBxMZlUYQEYkvA0dFeUhg4miEyMir26UT9zBFNv/zSj7ZvD8KYLL90u3ZuoJAaCoOu+ns+KOgtwOY5oBw4TW3bBuOZFAVqrY9jY0Mxe/QOH+4ICqpIKd/Rx7PK39Wt64A+3kH5Vd5W34LXlMqV3THmFeTu7qI/nGCv1nQPJdgkk3nH1649AC3WJewtLkHpDoSynBrKeUHkVCgCh41C8KaG5o8SGfkORohO8LB+IuVbUCoWNvw2beoJGo9FH6iMxP2jVIopu/7atX/j/pkDAx82hRYWjrSrUcMdkU2dAfDNgUMJ0GsNCieqnTePJPpaU92tWUOIkOZ8e6ZqxH/8v/94P5wRzi4rPvw2429lPTX0AEh0tJ0iGr74ZrBly2boV7/QiBFYmFWWa9c4Cr8coud3qexJNLdVCQSBZ65+/WJ43gC5UFkQcIY1Mx30tzvxGuknTBiGqy3F3lP5M44jJdq08UB0wTToy9/HGXWTJlVgQL9EOXPGOaX4wPbtTAn1M/T6qYrbKm3wGomQTgM5uHLlNKigXkjOR/nylZRonfLnLxCvTI1d78WLF7C51ALlHVOJRcrSOzhitkEDezh1RuHaxno1f4zXnm++SQt70m5EH+b/UNlwHxQeHgkbzyHo739gvpdBAxYmPedZT3Nzc8O+PgoOXkEAaYORky1SsjdxZBCmI9mXduzwpAsX0gBAGk2NGjW1SDYfBpZAb9Kn91Kh5SXQoES3KUYC4uZLMV+1mKglEtADINx29+7TQNOnwTAViL93MUAK5mVkzw6OdODwxJkzXbFY5YOHGRueOwEICYyxsPIG5dkzQsIrXZscOeQlCG/YUKdccTK1zZsdaMGCTxEhMRfGCqx8VlYuXrwLg+BAcJMGfQjZZcMch3COGKFTAHnzxJsBNiiywZMXb5Yh5z3h5Ok8T8PCxsZvvnFD9M1UGGc02L0Zdm7l75muoW3bUfCavQT5hUp5aeQMmQ1OrVq5IQHaQBhvS8tpIuqkIAkYbrotBUAiIiIRDREFhSSV6o12587jkevnBIw7QPAsKDt32iEZeGGAqJNVj+XWrccSnVTr1v5QkCLwTDc9IP6dfQ9dkyPWTAHYplvrzuzfT+B4tofCExVjvYivnbHzEyc6Ua5cnTDub42d1vSYFveQpgMSnSWoBJh+6Pvv+0I595MoOeRejCNjf/zRDeD90BiAvLh/5EpQ1GMJVKnSER64jxC5oU4e06engiGtFv6OoL9AdZ29b81J2ONbA9gQynnxOBKaI/aUFvYEbtTIFfvAEVS1KjhxbLAkFADChuMCBeaC5ky9UNiJqFKlTHTixE31nYkebFYCdeqUokWL7hLr6WrKiRMEiqaqtHjxX2a7AcMFGBRqIzrTTxEFqmGn7P1fv34GPIsuSAZzw3OjRw/Aur2IvvvO8Khl78eMcaEiReagL9t1SuTE44MHd4MN5wQiZ/ylCD5jDlhs21mxwok2bkwHB6thsO+MpOXL3wDEkC87tmEwOPXtt0Ogz3eO0dDcPigC9A78x1EcemcUjnbbvPlPgOlHERlyV3JOSZcuLb7b0lgj62NtqPahbowLWckHAYBYyReRQochAJAU+sWLacuTgCEAwi38/IKg9G+EZ/0uKF9RoIsIpcyZw7HwEJLuuYKr0x5ejZ+ApqkVwjahCaE0bNgLi+sNbBKkjxb/44SHX39N8Or9SI01daoTPAPq0Pjx3SzuNyEbTpiwDJRXWxGB8FYyHs6ZQ/AuZR7mj1dlUITpv7hwREx8UZqcOGz8+NzY2M3TNUoh/ydPXo1IoY3wGnyr2Dj6/DlBIXSHZ/1cbOIzpRCJiWnKkYC5Tbep9gx4bN16ApzD20CT8whK2juJBlAHZnohnLwiQrgbWERxw/R8Vat2wX3+Skpqa2oMxo6zx2aHDmkBVs8Fd3B6Y1VkH3vw4AUUyJ5QcvwQIi+v2bJluufz/PlkFiwx1hsD4vXrO2PuUTAQhxmrougYR6UsXVoS3qvjFLWzpLIl95Al1xFtrEcC168/RKTSEPz5w9HBPDjIo9661R7e1G6gshiIfUxMt0lx/1jP92rtI3n1yg/Gtk4wDiJUWGVh0PqXX3JgT+oLD2/1AAjvZUuUsAOAHR3vyDiCo0ULgjGUqHDheKt/qMDgR69eznAOqkVjx3b9cNzW3iQUADJsWHeAq96SrqSFTMqVywCd67YWXYk+bFQC3t4rQS08GHTCsF6rKD//nBb74lUAPc2jngMHdqEKFdbDdhD/c8TccCZMcIZz4WgY2jvEqHby5AlEH7SE8R4emCoKA7mVK3vBCH8Gdg91+20Vw9Cs6dGjRyGXCXT//g1pz58vX6Ck27x44YLI2DRw0EyFZ3ZHABidJFDpEqg8OnduAqclX0SRxB89cuUK0a+/esIJpB/0lO5xxp3S9kECAIlzC4gDiSgBAYAkorDFpWxPArEBEP0M2Bv/1q0ndP78bfAs+sL454gQxGyge8oLI76nvpr0unnzcURBTEO4PkIdVBRWlDghOEdGlCmj4w9m4KV2bTcAMrPAhfypit4TpimHa/bvPxPJVo/AqBeCsFGCLJgDVd312Jg/derv8BT/TF1HNtKaPdF//LEXDLtByNdi2aD37bODQTQ/7qEZlnVgY604CfTu3WcRfXWerl+/g/wMYfDMdwM3dGF49ZWEglFYdYSAjYnE6HCVbrp37vwHtCEzoMSFQRkIkYBdPP6koo9s277dEVFazojWqgae3A6IDFEWoXb//nN4uPYDkOxLzZq9kwX47d5tB1oeT/L2nqD6ucDP91q1usPQdA+AhFGxGT3I8x8wQMdNPGVKzGSVRhu8P/jgAT/X3UBpVxTXPYG5m6st7xxTBtaunR5rlLe8BipqKb2HVFxKNLUiCQQHh8IL/Q86cOAEDABvYXjUUTHw84B/C/fvEx0/bo9ngSuA93zIHdIfDiNecWYg7p84IhEHTEhg796ztGXLBOQ4Uref5u75Hi1b1l0C6mfOVJ8YmPMDjhmTCpQwESZGH/MwA/bt27NRTEd9a8zr2LAFAza9erliD1MVYGJ3m96/JBQAMn78EABKf2ji3c5gU4UKGQGA3DL8GsT7FCaBUCR34+j5RYseK/L2NxTToUME8L8o/fXXIbO/W957liuXF1FHvhZHf+ivy8+LTp0+BwX3P/pD0ivr5dWrF0M0wyNVNFi7dzMTRW04ha6L0b+tfwgMDKTLly+9j6qIBLiTAVEVxbCHyRHnu/MF/cfgwV2RCP40crX4gb0iWsqhppcBmKvgGEsAm9LSnTtpYbdYhud3cf3pGK8pbR8kAJAYX7/4kMgSEABIIgtcXM62JGAKAFEyC+bMLlfuJ/ACvwRtk5KWH+syVRZHf7Ci9OgRgaub4IHAdClMpWWHKItvoBDipBUW3mytWbMPhtD5iJgJgUey+kH+73+EzcR38IDror4zG+ihe/dJUMTUJ4du2tSdJk6chM2cSu4IK5YZAx9Tp3qDn3UnDHIR4K9/K/3uOLKIo484/86BA24AMNMAnGsHiqMacTa1Vjw9zYcmd9PNitmAAbOQPPYoFLmPuZBMDQjR2oj8cgLgmR50VFMUR2S8eRMAY89khKVfpx49gnD/f4x801+TDVjnzzMtoCsArpx4HQLPWISRqSwrVuyCAroQ9xFQBIWFx8QJCrdsIbQnKm2GdY49hlevdgQtgjsoWAYjtH4XFKiDikAXc8P76itPKHJ/mquiyTm595AmFxOdWJ0EXr/2B8ixC2DHGTxXn8Kw/A5/djCuZMTe50tQMNZDToRPTI5b3D8mRSNOxJLA0qW7EDE8G+A43I81KLVqecAbtx2MVIuwh1b+vNcPgZ/7DRu6wRAWBe71ENkRgEyfy7n9GATv0EGX987VVd+rDqS5CQam1audsW9Jg/1brw/R5R9r2d67hAJA/vprE6JSeyCnS4hqody4wYBWGch+j+q+RAe2LYEzoB4YNKgZDP5+iilOwVRETZqkAzXrAayJOc0KgqmMhg6tjehq8C1pUMqW9cK6fBuUychWblAOHjyAPefP2Jv7Q/8xOCHzLTACgEJe0O0PxjsnmV3adDX+3latmgtZH6TAQH9ycbFDflcsCpQaTmJfwIHxFym5uZ0ZYae0fZAAQGz6lrf5wVvw2LP5OYsJCAnIloAWAAhf7MyZmzDiDYFRLJgMlRs5A4HdEV4cJBnFDBPjXr6sy6WRPTshqZonDJMJb+iSM15TdSZNWoNN2Crq0oU3BeoKK4v9++ehnTvnquvIBlqHhoYj0VpLbKwCJZohNUPesYNg2K2BHDX91XRjtW1v3nyE8OThAAt9AQ6GYRNqeqhMCzZ8uCs2qXlo2bKRUGoMrA6mmyW7M3I33X37TqegoCM0ebKOzk6uIA4csAN1QAbau3fuh6THcttyvePHrwDYWAOj0i1we9tTrlyRknHp0SNHGDqiKG/eHOBLbgHgoKQmQBYDtuXKtYN33AvKlk3JSGPW5edzu3b2iHZLA8NYOEDHSHi/E/HznJMonjvnDJk4wphVEcaaDtL9N3DgTAAmu6Q8STF7U/6JvVcrVkwLACThvfPk3kPKZyFapAQJiPsnJXzL2swxIQCQ7duXgH6wF6IqnsimO4w9mzVrHLC3Ko01KBrRB/8gwXrsGuY/g/YfXsK83hHyaekcnAICHPDeFXS7WZCgtwFoTCsjn14q8x3ZyNmEAkAeP35EHTtWRfSvj2pJLFzoSCEh/bCXHKi6L9GB7Utg/frViKIfhj//OPkpTc2OQTSmZR03bjkM4FVMVftwfO/ePXTsWEcAb0AYNChNmqSHQ84Byp49R5zeBgzoAkfKbYjiDFEEgrDjTrt2HtinDqPWrTvG6TelH2BnMY4aSpUqlfQnVx4pbR8kABC5d4aolxAScEyITkWfQgJCAjElULp0fnB/toIn5GoY1oJle5AwxdVA7L2ZMqply5h9fvEFwWNYlxR9//5ARIa8wiYnY8xKVvTp+fMn8MhXD37wlDgZ3aNH2njIWJGIjA7l8uX/EDJrpxr84M4rVWLvdBCRJsNy48Yj0CX1R9SBn5TELr4pfgKH5EWLguHBdJXq1euFMPEZKRYEiU9WO3acBI3YMVq3Thn4wf1Wrx4NmkAfKZpj1aox8V0qzvkKFYog+mO8lOCP8w48fPgS3uXRUEDTg+oil+YGIb6P0qd/qwr84Enw83n06Cjav78kgI8vAdZehOeyD0Bge9AV5sJvsSiU3FIAxBGa9L4ULFgAct4PxTJCf8ji1zt3CNcx7XVvcceioZCAkICQQBJJIFu2jBJ4TBSsegQcteHrG439tSvNnz8MzhMDaMOGQOK9gZLy779Ec+d6Io9IX4DbTxDtfBXrXpAio+Lnn3NuO91VYTsDvaQLgPFBEvWOOa9hJeNMCXWzZcsOx5fsyMPioyrvIt8ba9a4w2u/bUoQm5ijDAk0a9YKFNefIJqjG0ANX3j1R5p0suJo84ULU4NyOCN0kpWgx/5SxhUIDlkRMJrDS0aj4uTEkQjG95Pjx89BMm9f6tPnBNgUgmQ5Z7Lj2C+/eGBP3kmAHya+I05S7mLO+85EO1OHX79+jUj3c3TjxmUAK4Hk7p4OrAbFcU8Vh84ak3LdVB/iuJCAkEBMCQgAJKY8xCchgQSTQOfOjbDBYF78JfCIDpQ8xMxEQyKig/l+CaABgWMyLv0LDxTrLKgACEa1KGxKJiDRKLhXrLQwFZg+V4DaIZqTm9q+ra39lSv/wdCrXtnneXl4wGyATNXsocKbtORS3r4NA0A4BJECfgCLlM2qRYt3FBHxDIrARFAdKDfQK7ua7dVm5Wnw4FkwDAVLzxtLZsB5PLZsuUZ//30ez70SlnSBZ4eDlNsjofP+nD17E1EYsEBpUJj+6o8//gPQNhRc73Xj7bFMmQK0fr0z9e5tXGGNtwODCidPOoDfXp7SbdBMvBUSEBJIxhJg8PjUqevIa3AZe8xrxGunu7sr6EkZaC6K9TOPVc++RIl8NGqUgyZjZI78bNnSS3shXlcmTRoAJ4pJ2EcEyTaec261kSM98dyeBO53d+TnK0Dp0hUAtcwF+v57hHJYUHbvtidPz3xWC37w/vHw4Ut05MgZunjxGqhFgyhNGifksMqD+ZdANE05GAA/AvsWiEBVk549f4NXe1s4iFlG78MX9/Z2xG+iEsAwhWiYqpGLxtYugerVv4Yzy2nQR02gqlX/h71iNJ6Z/nDKi5Kie+/dc4Sx2gPRyY6g1/0JkUg9kbfRWfa00qXzojdvtIvyev06Cs+jdEavz7lL589fi/3pLDxrZkEH8keUWRR+y3GrM1Wft7cTdCQPALPT4DRWP24lcURTCZw6dZLmzBlNT5/eRFR6JOwAgRLg5u9vB1uPJ6gT7RBZXhbU6CMQHV9Q02uLzoQEkrsEBACS3L9hMT+rkkCrVnVAS1IclFZjsIm4C48zAr0Re+rqwAxWyC5c4MRiBKMsge9Xdz6+SXTpQlBG/kO7owi/h5u/FZbMmTMRe49oUTgnipubkV2aFp1r2Mc7cBns2nUGyvAu+vffu/DEQewwRQOwSgUPjhygv6kBb6IqkvJo6rKBgSEALixTpI31mSYNewRFmr2msXbWfGzMmMUwNvgrBj/0c2rTJhJ0RNewqTxODRpU0B8Wr5DA5s3HoOiFyg75NyY0Biz79QtG7pDVFgMgxvpNiGO3b99Fokv+naovmTIRvX4dILujQoVywpjkhiiXACnKTXbDWBXZe9Xb24WmTCkJWpU9MFJdwjh84FnoSJ9/nhcRUkXwnRaTPsdqKj4KCQgJJEMJMPCxbNkOGFTW4fkWBrAjCFEG0VIuOfZWvnz5pEQJ6ePjjlxPP4O2r6JVSiFDBk9E/XkiQW2AtG9WM8gNG5yw3n/9oYtatUpTxowTQNM6DnQ1vqBSCkPuqg+nY7xhaptZs1xgrMyO6NFhMaKvmWK0Ro2uGJ+P4j3JFQTo/v67J+3bN0gTSscYg1b5ge8hb++9WFdWwhkhDFSjQQA7SEr6y1Er16/fBQ3YYUQ+OlGjRtWRy+BnGH9Tq7yq8uZMNbRpUzVasGAnvkvlazno/BHRkw6G7pnKLy5aJHsJpAUlw/Dhk/CcHA19/Tx0u3MAlO/h92qPfVsBULJ+id99ceh5yoHaIkWKoG9tnNOYQYJp9Ly8vEx+J+wI17kz5xVqDGqv2YhM3oQ52MHYHobf7jvy80uFaKpU2Jc6ARz+CfpsR0TMpTXZnzihXgIhISEAN3oimnAPgFx/yUYU0+kTG3zykwC3Awd2A7g6he/vZzhODbHonlM/YtGDkIDtSQBmCVGEBIQETElAqxwgsfu/desxFi3edATBOMXgBUmKVtashLBGXcJzpQnTOdFa69aZkQ9kudUpTjz/HTv+gVI3CaG26pMTHj7M/VWWkh7Hlq21fN616x/Jc569yZs1C4anBn0Il2Yc5No1or/+cqY9e5yob9824FSta/R7Y87rFy/mIIeMNiBIuXJuSAS+PtlEgAQEBMOY046OHlWXI+W//zi0Oxt+i4ut5RZKlHHExzvbuHEfKHrXZHvEmht0lSrudPDgcqumGhsyZA6U1+14Npubifxz5ct74vcmPz/T+vUHAMTNgWee5c/JrVvtQK/lDCovR6pV6y08WSOIwRh+7nD+kdOn3aCwOyBq6hvQH7Sg1Kmd5E/ISM347iEjTcQhIYEPEhD3zwdRJMibZ8/eYH8xEoatpzCshMDIb/oynF9t6FBXACOF8Awaglf53sume9X2zLp1fyMh+FzQeVr+jAwK4n22O3JuLEe0Rcz8Xxz1uHz5Llq8eCOiY95i7xYBB4DQ9x7eLjB62uN4BuzhW8DhqKLRfRtTKTZvPgB7QH/69lt5lDa7dvFz2xO0SxMlekdtpaauN3//YOkecnO7hzGGgArIdH+8zsyf74QIDE+Abr9h/ckdp3JC5QDRX4gjnX/4oTYcY26DfhgDklk44fxPP3kB3NoAwyO800QREpAhAS3XsNq1S9KSJfcAqsq4sJkq+/ezA1M9RKusNlMr5ikGOR9gEbhx47qUx8IDtAGFCxcBjSsS2ImSoBLge4ifW02b1kJEzm2sL2Gyou7DwjgfrCvoHCtI+RItAd4SdGImOhc5QEwIRhxOFAkIACRRxCwuYqsSSCgAhEPIixRpDrAiEMYoXR6PFSvUS6ltWzckBx+NcMlC6jvTuAc/vyB41f0kGaotcIyJMZp+/VzAk99HUj5jnLCCDxz10bfvDLp58wQ83oPjzSXA3pdDh7rAUyePRMEUmzrg9OkbUMaGQ6FUnxQvEF00bJgJ99xKK5CUNkNYvHg7POYXwkgvX8k1deWmTT3g3fi71RkfTI1Xi+PxKW6FCzeHV6c/PIvUX61XLzdEvY3Ac6Co+s4SqIdp0/6Eh/FyGHuipWS0b98SIiUIIIHyC7JiUqNGOoDca2Q3ZgW0UaN+MJ5dh3etPMOZYeccZce0iTPhvFqrluGZmO/ZAMhGqh07vKBsjzRqpIrZwvSn+O4h0y3FGSEBAr2dvWRE5ns/MlL5PS9kaFoCDH7Uq9cTwMcb7JnYczT+gq8Bueoc6X//y0rbt8+wOhCE989ff90N+fHuw/kh/vkYqzFggDMom1rAsaSpsdPSMb4fnzxh/vU7iF72wT1KlCvXJ3BSygsAIH7udW7TrdsEtLsHqtpgKTdZTE9e3aXPn+eIA1cYHHOCMnEonKHSmxxTUpxgJ5O6dXsCGHiGdUm+Iw7T+Hbs6EGrVk0EiBQTBEloAITlxMbEHj1aI2fYOUTTB5jN7cKgzdKlqbEHTweD8RrJgz8pZC2uaZsS0HIN8/ZeAUeVIaDKthzgZSk2b54WetFGKlHCMtpZ2/wmbHfUDg52yBPbAPmjTkH/UKbP8po9cqQr9LQWAEN+twkhCADEJr6mZDtIQYGVbL9aMTFrlgCHnX75ZT54n52XAJDatbUZbc2aQeDmPWeVAEjatG4YVzGE0B6DR5zl82Uu0mPHnEDl8JXlnSRQS1aYu3f/HQbTf8AJHSopzPFdyhN69Jw5IfA2vAFvtQHIlTA1RmJn9p67cEFnjFVrhD5+nMDRbH3gWHwyMnf+xInT8GRXtlk01V+lSiHgR7+WogAQU7Lg48wP7+ISpQn4wf3lzRtCd+48sWoAJEsWTlrpAKqPSICSBE9fXeQEgxkcldesmQ5YkPNbvH6dcC/l5KnLLpzwduHC4cgV1QOyfw0ARZ7Rki/A4EfDhvGDH1zXzY1pycJhFH0BpWsQPJ7HQVHOx6dEERIQEkgGEmBnjB9+GAyDyBsABvKfI2ykb9cuEobjJ6AQGm91ubF4/7xw4Qjw1fcCUBNA+RQ+tpYvd4SXc07su743+y3zs5iTrvOfJeWTT9IhCuJ32r//vGRUv337ARyfmCYnAnvDaDhuOIFeJopy584O2pyWAKxL4jiEb2WF86O1aKEM/OApFC5MANcDqG3b4dizL050IM3V1RWgxibsqTdi3R4J+sdgyNgP9F2E6EgiXtOZyuzkSWfQXTlTzZoN4RTwE0ZuR48fP0LUTzar/D6s7PYQw9FYAt9/3xz7simglAsBSGtZ59u322PfXkiAH5aJL0labdiwDrr/Ray9yvVZXjZGjGCgej2dOdMUuWlKJ8kcxEWFBGxFAta307IVyYlxpggJJFQECAvv2LErNHnyKBjLgxC1oVMW1Ar18mU2fhWldeus0wPgwYMX4FzuDhAkUErIbcl8u3RxQXhoJ9B91bGkeYK2WbduPxTeeVC6QmSBH7EHM3ZsanJ1rQ8O2A4xTnXtOh5e3UdU0/L88IM76AvGS+BbjAvY8IcyZdrQtm0vITf1k/j7bwJFU3WaMWOA+s5spAdznmucf6ZWrXagrpOfx8LctOfPt8PvvjtyINUzVy1JzrGX64ABMxGtcR4KSJD0WzPMHcl8yv/+SzAGEp09q8vPFJ/38bhxqWGc60Q//6x8vuy53bz5YCgyL2nQoFDwMZsWC3t/bdlCeG7owI/q1U3XNXaGabHatk1Lhw4tQsJMID4Ki7l7SGFXonoKlIC4fxLmS58xYx0M/euw5iNBgwWFnyutW7vC87+3VeYEOX/+Np5bw5AUPQB7wvgBHjZ4T5qUBs/x7BIwkdj0XmFh4QA87tOjR68ALkVLeUOKFPnMqvOxbdt2AiDGVERxBFu0p+XbbtGiVAB7qiIiuu+HuzAxIkA+XAxvWN4nT56A09nfoKQ8gZwGfjA2OtFnnxVApIgDjIanQSHjK1HDcl0GojjSqECBAuDW74P9d/VkQxtrKBfxXhsJaL2GnTt3DnaBJqBJ9kPODWVj5Bw2LVumR9tjiHr6RFljUTtJJMDPmkqVCoH+9oVZesH4BseU6mPHfkkbNhyIr2qSnxcRIEn+FaToAdin6NmLyQsJJKEEKlQoDFqVvFCGdEkEtRgKG+yYaspaS86cmenXX9uAFsBFSvKudJzLlqWCV3peeCxrFDKjdABm6rOxeOzYhQC1LAM/uOsBA8JAObGHOEeMYenZszUSY7qBIsHwqLL3hw4RFLosyQr8YAmEhkZoAn5wX+ztHxSkLuyc+0kuxcUlNQUGxm9Ykjvfly9Ty6IOkdufVvWYr71y5U7wzj0BsCcIzxcCEBCzd0fEyzIl+PTphGgJogkTCL91NqzErKf/hDyGUGac4IFdQ39I0WuWLOnBc/8HFNjvkbDcHYll08CAw/enrhsGZDiP0JIl9giZdwX44QA6K8J7RZeRKrMHdY8egaDum6a8sWghJCAkYHUS4DwWixf/JYGnlg6OvUrHjw/G3mOppV0kaDuOWNu9ey6tXFkQQIgbqBqNP4/5Wbx6tT3AfHckBP4Oe6zpiR6NwILgXEslS34O6tYKoDmsCEqs/FYNfvCYp05dhrXHcvCD+2jfPgJr2Sny8Qnkj0lSGNAoX74CjMojAObsxtp8CnScffF6EH9b6N69exIA8ubNG4zTB8nt30ifT2LR7dixA1WsWB60RPAUEEVIIBEkwPlnfv11ElgBPAFiy78gcBPsX9Mhuu1PAX7IF1uS1zx06CB081BV4AdPonhxpv67T/9xUktRhASEBExKQAAgJkUjTggJJKwEeEP+xx+D6dUrBykUW4ursYebk1MqLbpKsD7at68Pr+Z68Cx0odev5V2GjX1TpjjB0z8nlN3RVhmW7u29F5QM6jYwcEgDOBRECxasjyGYAgWyU5Mm9eHJmSbGcbkfXr3iPCPuVp00Xu5cYtdLnToVNnyxj1r2mY3Lrq4uljVOhq04mZ6Xl4fs32l8Ivj3Xyds8vPGVy1Rz9+//xz0GP3Bwc6JgiNlebnmyUPwrtPRZzAgYqyMH58Ghp/GoLGy7DfLfTo6OsBg0xoRJ6tB29ENtHpfIYIuEww5aalKlXQAYIogsXkLeKkWhmfzO1CrGBuJvGM//PAOeYuuEMtDFCEBIQHblsD27f/Aaz1ctXMAP1PSpw8CDecdqxRI1qwZJECjT59RAKa/BMWnO5LIegDQdQetlDtADw/Qf3mB0qgBbdo0B0nJ2yHqGmi2KPFK4ObNR9An/EHRFW9VsxXAWIY1NhTJ3fdJ9RwcghCBYQFSb/Yq8k9yhEfv3r2oZ89fsbd5DacqJPoyU/yRqO8msqPXqVMLgNsuMzXFKSEB7STw3XfNkNdhJf34Y2YAGk4SJaup3pn+dPhwFwDeeWjZsp0AWuGtI4rNSODIkR2gvUVCUA1K7dpBiHQ7qkFPogshgeQrAbELTL7frZiZDUggc2Yv8M6WBw/tUYRiqx8wc84XLAjrnJWXoUPb059/5kKS3wWgwwmCwholcdLHHjaiQkFJxJ7WbvDAqgJPrc5W6zG3Zs12eFwq5+6MPef69aNhzDwJwEcXhq8/379/K4Q136Rx465CiQ9DOL7+jPnXp0+ZxsIN7foQJ/BMbqVIkVyglXhJZcuqn9mVK44wNBdW31Ey6qFmza9gYPoL4ID85KfGpg+HSnr5MhXuwczGTifJMQ4779BhNBKk+iFJq7IhcGL0+fNJymdUqRIB1P3Yfts2e9BsZIH3bLOPB1W8c3ZOjd9wLekvdjfBwaHIrdQaCnLsM8o+A4+HR2wwlOetSL7ZSVnjJKz94oUvXb78HyJj3gJsSo0ons+sLolwEopHXDqFSuDYsVOIBnuryeyrVmWDyiWrA6/1k2NnoqpViwEULorI4nfwfn2OtcYXHPgOMN5nAYDjoa8qXhVI4OTJq6Bl0eYeqlQpAlRYZwE6fA3w42tpFNHRdiYjKBUMU3HV4cOHAQzbiDVDWbS8n58f/fJLN0SQeMMJobzi64oGQgJKJVCpUmWAbmfgFDcdYO5q6Pfv4OUfiLxEEdJv5+7dNHT+vAvyv7lg/9YdeSDaw3GsDou9AABAAElEQVRGmPaUyjmp61+6dAa6iDajKF48AjrbEUQCtdGmQ9GLkEAylIB4SibDL1VMybYk8M03VejIkdNA/xG+obKcOpWGKlRADKQNlObNa8BDsSSiYDbAQ28/FNVoGJ/DQD0TDlojBySAcwFXczQMi4WRQLKV1SrfLOrIyHcIl/eH4U294DkKJEcOO0mJZ+VdX1iZX716DDzCZ4A+4SSMq0FIKq0/G/cV+U/BA+qAnDBuNG1afwBtpeJWSgZHypcvg9/PRQAg6sGnY8dcALYJAMTwtmjf/jtQduyB8T1QVTL0FStSQUFrYFXRW97e+yhPnmegvzKcsfz3DILMnEkwihAS3erarVzpAANJZtBRTZIMcPJ7U16TPVnXrz8M8DgM0SYEujJdcvOvviLiv6JFkc4VwIbcUr16FCLQwKFg5YWpVJYu3YpE9TtBWxcB0OMdcsu8g0HLAWCWA7x6HeBxXBPUJY2InQxEERJIaRK4dOk2fsvazPqLL6Kwl7iCzhpr02EC9sJgCO+bDPdOCXi5ZN31lSvXoE9EaDJHToh+9epDPKPLwnCbNMAHT+Sff07RmjXeisEPvRAYBOnYsT36OYN1101/WLwKCSSYBNzBzduv3wjq1WsIaE+vQS++QHfv3sfezh6Ok/mxPy+OXHP5rGpvnWDCSKYd+/r6qaa/0osmY0aCPeKV/qN4FRIQEjAiAQGAGBGKOCQkkJgSqFevLA0b5gSKojBwBFt+Zaa/2rvXCZzNGrjCWz4MRS0zZUqLEN+OCN1tj7wXT7CxuwvvsGBsBFLBYJ8DgMhnSP6rQiiKRmN55YcPX1LOnDJDMmRcJn/+d1IekNhKPFPicCLJgwcvgh92Dox//lSvXhA8gqIpUyYGYgjtCAkdnRAtkxpeaiWQ2PiXZO0B2axZNUQHrQClQTg8n2QI10SV+/c5v4IHFS6cy0SNlHmYKUaqVq0IoPJv6t7dMpCJ6Wj/+suDjh5tYDVCZPDgjz/WwZCuzsMVOVIpF26Z5cuJdu1ypSxZitGePX3J09M1weaqBz5GjFhBL174gMIjnJj7WV927iQAAoQxEA0ZQuCRlgeEZAHe+uyZr74bq3zduPEwkh7Pg3dbMJ5xkaBoiztMBoLWr9+INWQ39enTDvkBvhHGgbhiEkeSsQQCAt5Kv38tpsjPkYAAZd7yWlxX9JG0EggODjEamW3JqFi3iYjABjUJC6+bHMHBlFZqCucImTRpIihpx6rpRrQVElAkAY7sKAqvFv7Tl0gofbdv34IT2BGwAthjL5oT0SHZxX5HLyAbeWVaxghgzc7O6gfMtiBHR3hSiiIkICRgUgIqzEUm+xQnhASEBBRIgDniGzeugRDXHTBqW+5ttWCBE/qpbhOAQWzx8MaN81zwny0WVuw4ckOr4uQUBW5/08pitWrFET2zWOLlPnjwDHJ7XHpP+WCPqJAcSK7JwMdXMA66azUkq+2HDc316lUGHdE+iw300IsBwrkiCXQ7q51nUg7st986g17kPKKwXsAjVNlIAgKIOnd2A3A3AMYUDXb3yi5vsvadO08BIAZDWTRZRfaJFi34/vFANMgQ/C4TNgLPzy8I0Q1jQHtwG0bJEKNj5OgvX+AY/NevHyEihWjFCqK0aY1W/3CQo0Xs7cE7aIWFjVejRi0C0LOHtmwJlgBfU8OEwyQiYt4B+AnA/BdDVlfx3fQTRgFTAhPHk50EnJ2diJN/a2FQ4Rxbzs5pkp2MxITMS8DNzdVs3gHzrWOe5TQbTk5Ja3I4c+Y01kS/mAOz4FN4eDgofNdhzR+BOWm48bdgLKJJypMA74U4afaSJZPpzp2b9Pnn9nC8eUe877t3zwH5jqLhtFSTfv65F3RqeOiIYvUSyJMnH6jQ7yOHlfqh3rhBiAhKWD1E/ShFD0ICSSuBpN2NJO3cxdWFBKxGAsOG/Qwe92OggnpNhQopHxaiYmnjRk86duwn5Y1FC9USSJfOHcnsYUXXqLx+7YiojfjBC04oXbr05zDstQKtQDQiQKzTeKmRWEx2M2JER0SBnAaV0SuJ9sdkRRMnvL0dkFumADVsWNFEjZR9mEHajRsnQz69ARL5IuRe3n3GUTWdOrkCmOoEhayYVQnx7NlbAApDNRlTKbDLubg4Jzj44esbiPu8l0SPx7R7cgoDUCdOENYWon37yGjEhGE/UVFAQaywzJmzAbSIu0EDGCI70osBn4ULQ3DPnkBek0UAUDpZ4czEkIQEtJdA4cK5QTn02GJ6P8MRXb1qh8jIgoaHxPsUIAHOh3blygGqXdtyxyy9mK5eJVAV5tJ/TJJXpr7y84NXgAblHazNp0+fxnos9owaiFN0IVMCjx8/AhVWO+zj7iK61Z9KlIgb3cuRBDt3/gmHyj2gBm6IKOAJALCtx/lI5lRTVLUvv6wCWr1DAEDUP2vPnPFEflVw4IoiJCAkYFIC2nG2mLyEOCEkICQQnwTYwLhs2WgkwXKXKIziq294/uZNQjsPULn8ZpPRH4ZzsdX3GTOmJR8fO8kDR4s5XAHddrFi1p/MXou5atEH/35Wrx5LXbp4IipGWY9//sm5VbIgggRcQaKYlECOHJlA7TSXNm8uQj/95ArDiMmq+C0QzZiRCjRF6WjcuOHUokUt05WT6Mzdu//BS8oySq/YQ2aKGE7CnZCFAc5GjUYqAj/042HPwAcPiH78kcwmneXvLW1a6+M1v3HjEdbH9TR3rnzwQz93BBcir89b0EPupbNnsViKIiSQAiRQrlwpUA5qQx968qQbHHS+SAFSE1M0lED58kVAraNN5M/hw06gZC1t2H2ivz99+oxm12QarbNntetPs4GJjpKtBC5AuWHK365dL9Dixf5UsmRc8IMnz7npGjYkJE/3o4wZ18FhqQoin3yTrVySw8QaNmyCPFtuZvfncuYZFMQOT6kAzFaSU13UERJIsRIQESAp9qsXE7c2CRQrlhs88uOpXbsR8JoOBG95JDz7TY8S9jDQmjjCw9Udr6NhMM9turI4k+ASKFu2IB0+fIqqV1d3KTZUOjt7WhVdkLoZJU7rQoVygpZgMn43w+mbb/wQdRAGOZq+9ivkiGPaq+Dgz5Cw+rcEzdlgehS2dYZz9mzYMAneZadp9OjV9ObNc9BiRQFIYMN0NPJRpEYen9QIwXdE0vRvYYD7DjRTLlY5SY6gAKWyZsXcs1qLiyxfvheg031EecmL/Ih9TQZBrl/X0WG1aRP7rO7z8eOkqaHz3r1nuF/2IyfRRSTtfA6AOAr5ndxxzxRAfo5K+CshK1H8sGGzAKQFmf09G5+R7ih/z9OnB9HAgdPp77/nm6sqzgkJJAsJNG5cCb/lRTRgQJhkELN0Uq9fExLvsvHagtBkSy8q2lmFBPLm/RTjSE83bwZS/vyWD4lz023cmBr7AYQhJmHx8Xmj2dXZIeEGc82IIiSQCBL4D4n0fvmlKehM34DmWN4FHRyIevYMQ16Qe3BGqgfnpcNYC4COiKK5BPh58PTpU3r+/LmUiyVHjhxgcUgv+zqZM2dGztPyyJW4G1Tmlu3x+WJ//OEMkKyt+J5lS15UTKkS0FD9T6kiFPMWEtBOAiVK5IOSsJgGD54Nyphz8NgNAYXBO3B8clKrj0mujxxxoDVrXOirr0qifg9hvNXuK7C4p86dm9PYsVcAgMAFQ0VZsiQNInqaqugh5TYtWDAHfg+L4PG9Cr+fPVSjRgQMN28lWjkGQ9gJ6vJlogMH3GAMTgNqnNZSdIJdQluvk9FXwrKqV6+s9PfmTYCUh+bOnSeSYb54cS/wDuejPHmyWH2+hUyZMkJZ4SBYeXRe5r5Cphyws4O2mUAlKioKFE4rTeb8kHtZTg4+aRJBGeZcH3FbrVjhTr///l3cEwqP3L//HPQMU0AL+ICaNw+CETYaIJluDXvxwhcg2UMkKT8OQCI16BnaQ2GrbvJ+efToFQC1+/BoUziIWNULFyaALW/w+/8PiuZnsc6Kj0ICyUsCnG+pQYOqiGzcQz16WB7pNmGCCwxvzSWjTvKSkJiNHAkMGNCeRoyYSOvWBZt1yDLX1/z5TnBKqSTpKaHasE6au1yinWOjpyhCAgktAd7/9erVBnsz+eCH4ZgaNnyHfc8D5EEbj5xoIw1PifcqJcBROUuXTgV91UnkYbGjrFmjCV8XIrWJ/P0dqFatenBq7UG5c8fvoDpmzExJrypf/g198onygbFuu3NnRtq1q7/yxqKFkEAKk4AAQFLYFy6ma/0S4KTO8+YNgje1L6h5dmPTcxa0WE8RGhkFBcQeYMinAD5KYZGrQ5kze1n/hFLICMuUKQAjQQ6EHV+nOnUsU4w4l8vRo540YUK1FCI17aeZOrUTDRvWHhv91lKkwunT5wEW3qXQ0DDy8HCl4sULI8KqJIDFoiaNrtqPKnn2mD69B3JLlJD+bG2GJUp8DkolVwwbqIDKwjSEBQtmV9mL6eZHj16mt2/DTFdQcIaT0R47RnFyAxw6xABBFuTQiV9RM3e5dev20cSJCwEGBwKEjFuTFTv+q107mF6/DqZBg+bB6+1vKJEjkEclLt3K3r1nwGf81mLjm+EIvvsuCM/nEwIAMRSKeJ9sJcDrYMWKJ5Gb6DXyLyif5r59djDkZIFX6bfKG4sWyUICtWuXRhRfcVq06B/smxDKobBcukRo74l9bdLnX/Lw8AAoj9BfDQo7gsgxampwKdFFCpfAtm1bEMXxgCpVslwQ/fuHwDlvObVp05UyZcpkeUeipSQBH/DFDh7cFXvY06Ak86NZs+I6FYVhy75ly0o4NG7GXrgpHIFGI9dk3D2uXqQZM2ak8eMXwPG1PdgM/PE96c/E/8o6SKdO6WjJkrWUOrU21JfxX1XUEBKwXQkIAMR2vzsxciuTwMuXfghP3Qnj0hm6c+cZRhcF0MKOcuTIACqCLyVKmM8+kw/rM7jRt++P6If/RLF2CbBCNG/eEIAf3UAXEECfKXQy9vMjeFq6wdgwBBsYJ2ufrtWPL00aJ4QSV5T+rH6wYoCJLoEvv8xL58/bAVhgyjl1l9+2LbVE6aSuF9Ot9+07h+gldZFl+t75OcNgR+XK+iMEKjOioUPdQZGgLg/O8uU7oYAtAvAYDLDxY/+m3mXIQOCyDoZx7TLym/SnrVunxnn2nT//L9Wtq9zwZuyaxYtHQ1H9F6daGjstjgkJJCsJMKC4cuVYRDkOAFVqgCIQ5OBBOxozJj1t3z5aRH8kq7tC+WRmzuwPELo3aFUewZvZPDWvYe/nzhGijzxhzBtnFNw2rJsY70uWLAUqxruaXIrBlDJlymrSl+hESMCcBDjCYPJkdY46bBNv0yYYNopFsCsMNXc5cS4eCdy5cwdUy9+CXuwNopdNPw9Z5s2aRUEH9UNOxFXIxXKY1q7djQT2ph1Xa9asBarYhcjh0hVR376IIDHvTMlBaN7eqWB7SIdoz/VwxBJUlfF8feK0kIAkASMkCEIyQgJCAkokwMlve/acDI/WDli4vGFIug4QxI9OngzAnz8iOO4iEdkm0I70ALI/RIrsUNK/qGs7EsiaNQMMer9Ry5buipJx379P1KSJG7yhu1OpUvltZ8JipEICNioBBhkbNKgM71R126CQEPbycsKzvWaCSeLECYSGaVj++edjZ+wQ27y5K5StngBt5QH075BQZOfOf0BhNVMCfEuXbk2lS7dCH7OhuAV/7Fzmu44dI+Ak8IBGjlwQp8WzZy9BKxDnsEUHsmUj0J4h07soQgIpRAJFiuRCBOTv1K1bBpo924mYrs9c4efZiBFpsG/NRtu2zaBPP01vrro4lwIk4OqaBvfCdDp1qjgMf66gJDQ/6WAsARMnpsb6kAXr6xQY5XKYb5BIZ5s1aw4aLk9NrmYPDsmyZQUAookwRScmJRAQ4E+BgU9V5eDRd96kSQTt2bNR/1G8WiCBly9fSuDHnDkvsG82DX4Yds305f36hVKXLndhG6hL4eHmKSlr166D5+Zh5IQtCx3FC/mT7GA3+tgjgx4PHxLOO4Dm2Qv0W3Wk/XjRokU/VhLvhASEBMxKQESAmBWPOCkkYF4CV6/ex4I2FAtiALzlIuLwqiMoAKGr/BeFCJBAGMougi6mM7g4ByEctYT5zsVZm5QAJ0Nfu3Yydew4Gvkn3sDwEGYylDUggOCh6Yj6HrgnBiJMtpjROXPi4+3bT8FDei/48+9+2EBxqGvx4nnhPV2bvv2WlTHccKIICQgJyJJAnz6t8Bw+Cn5yf4DUsprEqTRunDO1b9+Y2EiUUCUgAFZJDQs/d1iJ2rbNHnR7bjBW9Qb38FfxXoE5z5cs2YYoirUw/oQh6iWEfvqJKG1aAsWcLsn6iRMErzUC7Q4hvweRu3u83UoVevcOxzPsMIDjOkiSjoQh7wtH1jGnshaF++H+RBESSEkSYBDk2LHFoKVbDBqVwzCqhIEaK1yKCHFxYa5yXW6sgwedaf/+VMjj1BAAyA/IO5dweY1SkvyTw1x5ffP2Hgu6wiPQd5YAlA6B8S2IihWLhkczSZGUTOF68qQL6K5SQd+pS0eOtCAnJ+tJulylShVEorjgfscNr6I4ODhgrfpW6ktFN6KpkEC8Erh06bL0G4u3oowKnJM7MNAPIHiESJItQ16xq/D+t1+/DgB2X1EJC8w3jRq9oxs3HtC0aWPg7DgmdvcxPmfNmg3P2110E9xWa9cuAEXscdD3vQZNLcHRlvD8ZSr0r2E/6ID3GnkIxRiB+CAkkLwlIDTB5P39itmplEB4+G6T8Yc3bz6ipk370YIF/uAUl3+hJ08IlARuNHXqcIAhX0oGGV5YIyM1svLIH4qomYASiIiIRDTINhgMN8GLMgxGvVB4WIdLXPZPnjjSxYsuSMRtD8/xOqAJaAZDISwRRgornCNH/oHNThjutxDpXnPl1AUo7GnHHMv/+58rvEBSwwO7GzywVWYL1nUt/qcQCTg62qfoZxD/vubNmwklIxj8vMq+9M2bHeCFlRNedbOhmCScsbBSpV74fd9QNjgztTkSIn16d3jmFoChszdycqQzU1t3yscnEFGMQ/Ese0yjRoVIoIepRpFgrFq+nKC0Ec2ZQ7KVxYMHCd5uZdB29Ieu+/adBiB5LyIsPxyy+M3p0wQKiDK0bNnH/i3uTDTUTAIp/RmkmSBldBQYGEKbNh2BN/85unr1HgzXEdh7pEHun3wARcqC6qhcHBo6Gd0maRVx/ySu+FlfOXPmJgCOC9jHXgKgECTdM4UKfQ5gvDiA8RImgY/Q0ILQdTwkAD5xR6272p49e8DZ31kVCJI+fQYAPaewhoroqKT4Dq3xmgn1DFq//k94+/ek/v3DNJl2gwbpQYd4HPlDM2vSX0rq5PDhw9Dp2yI3q+UAKgd/1KiRFvrGsTjARULdQ9b6HaVP7yVs0Nb65aSAcYmbLwV8yWKKlkvAFADCxu3KlTvBe/YpKIuU988h5E2bpqXjxxdjE+0JZUAAIMqlaBst+Lu9fv0hPJvv0P37D+HNHAUjYhYYBT+XkvGa8rLke6xr1wlIsnYRYFkwNqzm5/vsGVGfPq7ouySSOw8U3pvmxSXOvpdAStt0G/vi58zZAM/Wdci/E4zfj7EaMY/hJw1g05HWr8+MyKwZlC6dzDCHmN3I/tSr11yANNtk14+vYvnyBUGLMwxzlWfA8fMLQpTMr4hme4FoM7ifySygSgbvNEkJIuWskxyhUbGiO4xLqz5E1KxZc4AuX54Jakn1BoD58+1hqGsPwPl7mTMQ1RJDAuIZlBhSTr7XEPeP7Xy3SQ2AsKQ6depAu3btohDme1NYPDw8AerPRcRkPYUtRfXkLIGEegZt2LCeHjz4VTMApH799DDinxCJ0C24Gdu0qYe94wnk/rGgsUGTZcsc6dmzLkiiHjMKJKHuIYNLUwDCv48ePQrg+jg9fXpPcn7LmjUvooy+gk2rMrm5uRlWT9D3AgBJUPGKzuORgDry63g6F6eFBJKrBGbO/BN0RW8sAj/YyMN0K926+dGvv05JriIS83ovAaZcKVQoJ6jSasCI9xMNH94elDl1ERGS1yRIwSBJmzYjAXqchcdy/OAHXypLFoJRMxh0BP+AluY3CVQTX4KQgJBA/BLo3r0p9e7dD3l40iKRoAOUBONtGPg4eZKQ1NANoekVpMiPhAY/eCQ1apRAUnHjEWLGR2r6KPfTu3dT2eAH99Sly3g8s5SBH9wub16CtxwnwtXR7PAxcwW06lK0CNP86cs335QGv7GTFPavP2bJK393HCnXsGElS5qLNkICQgJCAkICyUAC8+bNpwoVKio29nH+kFGjRgnwIxncA7YyhezZswMA0Wbvx3N++TIKDjvxR/zainwSa5yct+P27evIdaf+it9/Hwnd4S/1HSnogXOX9OvXEZHUxRFN3gU2iT9AdbkHtoLdyC8zh06c6AxGkqKg9+pKb968UdCzqCokYJsScLTNYYtRCwkknQQ4H8OKFduwgMnzSNUbzdasIeRvIIR/E8LDdZzp/v6nsCD1RHLK/qBHggXbysuVK/dhRNpHZ89ewqbstTTadOlcYczPj2S4VcH9XhJ5UASuqvZrnD//Lyhn18ATGiZRZsntj+nt2VO6R4/L8PLZijwkDeU2FfWEBFK0BL799isYRYoAANmIZ/JuypkzGrQwoZQhQziFhdnTrVsudP58NGij8oJqrjVomQonmrxq1y6FNUOb7Rr3w6CC3LJ58zGsWTeR8FF+5Idh37lzM9hPMBwRTZ9ueMb4+4IFg+natQcf5Ovl5Y4E6cWJx/H990AxLCyHDhFly/YZ5ciRycIeRDMhASEBIQEhAVuXgKOjI5yF1iLicx7WpGnIixCINQ6KmYnCeUPSpvXCnnoxKL7KmaglDgsJaC+BIkW+kOwGWvTMibS9vNLD8U6bvaQWY7KVPq5fv06FC9sp0sdNzc3Dg/N4hIB+8i05OzubqqbZ8W3b/qLx4/tT376+NGlSFL7/mF1zdPb33wdJe/QNGzYgv9E+0G7PhE1HRLnFlJT4lJwkEOtnkJymJuYiJKCtBEJCQmnv3nOgStkPI3+AZNRhR4piyFuN3HqSt2vsK3JSwN69OWEVIXEg0eTJhAXvYy0fH6J9+66BDqsTDGvFacaM/qDEwupoojx79gbesP/AEHcRvKDPJS//7Nk/AQBRjOrWLQsDD0JLEqCwQapXr0m43ivkrAiEB7QuuTtjHa9e+SKc8jHoYE7BeyA1Fs4uOF85AUaRMrp8/twHeWXW0e7dby3abDEIMn78W2xeVsPbuQpCnZGhWJQUI4HHj1+BX3YvnT59AUb7pxLlmrOzE4z5/2fvKsCjONrwF4G4oMXd3a2CFi2FAgVaihQo8pMWSosW9xb3QgvFvUhxKVCc4sHd3QLxEJL//fZ6cDnd3dskd2HmeZK7252ZnX1vb3fmk/fNg/tUFUx0P4J4p0Kxi3cEPTa29+vXDvexNnT16n2Jtu7581AsGt2RHp4N9/q8cEwa3MCTCBcWku3c+RMYa/4EbYc8x7u5oXl7e8Ap2sAiP7u5NhMmzAOlXriqe5G+v6++IvBO87NCl/2o327u1ds7DgvDhOc4bNj/EJ12Ct/BS1Vi9bBvIfPOD8/unuYOKbYJBAQCAgGBQApC4N69e1ivbYbx+B+6c+emdGZZsmSnUqU4UKsu1mTZqFu3IKxVmkqOEKYaeg11Yf7jDGw3NzaPxEvR8kFB32GN9nmSGCtT0FcgTkUDBJiS6L33coEG9LkirVFzh16xwgPG7ZbmdoltNhC4C/HWHDkg4KFRyZrVlR7CI5UrVy6NejTfzaJFv9Py5cNB1fsSwVzm6+i3pkrF+rSvkXH+FPaqbsiEf07Nm2PyLopAIAUiAFOZKAIBgYAlBFgDJCwsEgblObRu3T8Q94tFCmQkFShAxA8LZBXC+E/gkyXJq96/P71JkVy8mOi333RRr6VLWzqCbjtniaxY4QIHSIAkAFu2LA5gUG7efAjD3GQYNC/Tp59GgiYkFg9OXYXbtwmGOjeMz5uyZ88NXZLulD8/PC4aFY6I/v33pXDehL05N0tdc4RJr14+4G8vgjYDnU5M09J5JeX2kSP/gJF1FShn1EVc68fKVD5xcS2pd+/W+k3iNQUj8OhRCFKcJ4Ca6TyclOGIoI+T7lMc7cMG4DNn2NnqSRs3pqZ27RrCMfulRMGWFLyzKRj2JDs11gQqUaITHDP3VB8zb94sFBw8G88uebEvFy7cpu7df4Aw+UvVx9Q3nDmTcD8iGJ30W8y/Tpzojuyb70ABWDtBhXXr9uEZNBHPyXDQgSXYZfUD07y3bu2DhV17LOoaWK0rdlpHgPWsjh27BAHts9C1uoDMqBgKCPDHdVmMPvywOOXJoy6LVdyDrOMu9lpHQFw/1vFxlL2s//H6tR8CqVzwlzijun79OjI0uyNA7Az0qsKwVnqF54nuWLduEYLH3OEI94MDpAginqfgnoUURRS+hh7DQ8+R3pGRUXjGBIAmpgjWMj66xuK/QMAKAol5D9q6dQvmYJ1hT1A/D+N5UI0aaTD/PyoosKx8j5Z2bdy4EXOfLgjwDLNURdH2li3Twa60Hcwfud+00/oaOnz4EA0Y0BLZ0y9gU3hzGFlvmAa4UaNA6I+uRoCtDQOWrB5NKwkNEFNMxJakQ0A4QJIOa3EkJ0Rg796J8Z07D6evvgoFV+IrGPQtn8Tp0wQDJMHwSJLhcelSInaC+CnQx718mTNF/JGe/QsVK5ZLOtjChZtp3Li5ePCGwgFDFiNxeUGxZw/hgeeHsX5ODRt+JNXNnDmtakcEa51s376c5s6NQNS45XM33MPjmDo1FYwk+TFp+0W2sc2wj3f5fenSrTBJfarIyGcOrxcvCM6y9Ji0LTK3W2zTCAGOFrx58xGcDBGIEPTAhDaT5FjQqHtZ3ezYcQwOjZ+RuRCGSWucxXsEdxYVRUiD9gQPbAZEBo2B0zQD6rNBIh40ELBQi+KwCFy5cg/aUz3heA9RPEbOBNu1a7wi5/i8eVvp9u1puLZeKT6ecQN+Pk6YQPTHH8Z7En5u396f+vcfjUjdvAl34NPSpduxIJuFQIEw7DfZbbLh4kXWH/HB8/tLZGw2NdkvZwNHBD98GALDXRyyM/3eyewpvsfNn78ZmUDLEHgRhTlOODJW48gTiWQhuBSDg1PRzp2ewOY9UDB2gZB9MTnQvqmj9cL/TcfizTuBgLh+HP9rTgrnB0c7//rrSBo+PISqV7e+VoIOMJ4zaRBo1AsBIV0lB4iYBzn+deSoI0zMexDPzVu2rAPNhmPI7Fc3R+/Xzwdz/Z7UtWtPR4XQocd14sQJmjGjGQI7QduhQfnwwzRgeTiDOdNbw4qW11AUFnpMST5nzj3JHqVmyGfPEgUF5QDd+1HYcRDxq3ERDhCNARXdKUJAOEAUwSUqv2sIFC3qF//HH6Fm6a3MYcE0sgMHEq1fT7RvH4E31lwt69tYJ6Rnz0y0d+9viPhYS3/9tQxGI9tRrzdvErJHOMqbKDyc+c49YJD1pHv32FDhTY0b18AEqpFsSqRDh86BM3IgMkvCJUOH9VGb7h061ANG/IbAo6PpTrHFLAJMt1O/fgd8h+ojfQw7rlHDj/7+ez6ccG8nWYb7xXt1CLAO0MaNhzEZXi5F5OfM6YpIaN3v7vr1OKSsp8WiuiloE6rht5Na3UFkttq+/Siyw8bAOBxGmRUEYP/9twsiJdPTjh3TpPFq7QBho2lISLjkWAkM9AGlhJvMMxLVrCFw+fJd3CP6wwnyHJGqtlPymf4sY0aO/BuJhVA2a12b7OvVayJVrrwVCymTXYo3QENSMkrt32+5KTvnqlb1R1blEouO81OnrkGUfQQM8C/wGwtHdFpCQxc74BHEi2emN2jgfGC07wcNkSKWD2pmDzszlyzZDgfhRjg/nlOmTK4YD9GTJ3HIYkmNSMpy1KnT54rxNHMoh990795TBFQMBv3DPWhSReBeYXnI7OTq398XzqkqcLJ+K5tqTcuFv+XRiT0pFQFx/Tj+N8sOkNhY/0TL/JgxYzwCwKbA4PdSdrQzr5O++cYfme2dEbw2QASCOP5l5LAjTOx7EFO6NWtWDfTIj5FxqQyG+fNTw45QDPOZbWIergy6N7Wjo6Mxfy1EBw8qDz5608l/bzgjv0GDLPTPP/AwGBQtr6FlyxZjHtyHhg3DTc6O0ru3HwJeJiOw7jM7ejHfVDhAzOMitiYNAsIBkjQ4i6M4KQJwRsRnz65s8GyA6YkgC85s/O47ZW31tUeN8qCwsGpI196HdO3wBLoh+jr6V+ho0ciRRAcO8GSeo/4Jadv6vbrX58+ZYsuNFizwBh1IY0T0trQ6EeKo14oVv8Zk6xEVLJiwL7mf2OBVr54vnDITYKzKIbfZO13v8OHziDIZBKMdZkgalM6d/eHEGgkagPwa9Ca6YAROnLgiGWBLlHiJ6MEICOMlNMByHbAwIGLaE44sb0Ss/wj9gjK8WVXh3+KlS3dBYXUD+g9RUgQ6Z4cVKJAVBtmXyArrgkyrF4qcH/qBrF/vgkVRYfDDTpI22ZsBwg48jhT/668dkuE4XTrdFOPZs3jwzwaAf7g6jNafSO/1YxCvyhFg6qEBA+bRwoXbkdETY1YXhPU+2Pn21Ve18Hz4WlUWYLduo/A82QNxeOVjNNeiEvRjDx0yt0e3bflyF1zr9WA8t/7g5N8E63GxA/L8+VtwULjA2e6CZybR3btxlDdvZvw2m+N6q2TRkWJuFOwEXLRoK40ZMxfc8NHUqlU0AgkS1mQqic2bXRCc4AOKlNKg5eoB56fRAzdhE6f9dPv2Y3z/PWjQoGe4z2BiI6Pgq0HGampwlufHnGOMLCeIlgt/GUMUVVIYAuL6cfwvNDEdIHv2/IP7cFtpHmQtS98cSrxOadYsAGuiObjH1RaZsOZAEttsIpAU96CLSGnlIMbvv3+Ca/a11UxvHjAHlIwe7Q0dnAKYK66HY1AhD5LNs363KrRqVQeO0n+xnrbvvBcudKMbN77BHH50go60vIZ47jt9+sU3VOkJDqTgA2dR9+1bnFavBr2IxkU4QDQGVHSnCAHhAFEEl6j8riEAkXJ5q34jYNgpUacOgTrKvDi6UXWTj0xfVKGCCzJJ4q2mLz54wKJVBK5bkkTZbQVZ87hGj/aic+ey0cqVP1vMDNiw4RAyP8ZCdNe+6IENGzgTpjpNm9bH5BzFBlME9u07AwPyUOCujQPk22/9IJ48VHEEtOnIxBZGgCl4JkyYRZMnh8mKwuKsrO++80EEfSNJWFsJirduPZIoZzZs2AfaIoKxNRoLmFgYed3p7FkPunKFJMqtNm2eYFGk6jYlDadrV29E93yLFPuaqimwONtj6tSVMAqvgv5IFBZnr8CxnfBs+V7155/uoAX0hFH+UziJWyU5VVjCETn/p/DwKFq16h+k0h/FIveq5Ahhx0eJEnnhfC6H76EqnOHgKVJZ+vadgsXeJmScqOzAoJn+mcg0jeYKcw7XreuHTJUZEi2buTrmtrHT4sGDZ6Cgi5TONUuWdFIkr7m61raxxso334ygV6+C4QCJgPC9tdqQx8VPbsECdzhhAmDo/0URtZj1nh1jL+NRtWpnLNLvQnxe+ZjGjEmN67E67pff22ys5cLf5sFEhRSHgLh+HP8rTSwHSCQeLB9/XAYZew/eaH0oRePOHYLYb0ZQRJ6SKGnsDQRRenxR3/kRSKp70NOnT2GQ7kLPnx9DINZzZKMSghkT4seBIKtWuSMbyg+2gY6gAe2NubZ7wkrik2IEdu78GwEyHZBdDAONyvIKbLK1agWinz0IrkkYXavVNcT0Vx9/XAS2F0S+alAqVUoDRpJLml9DwgGiwZcjulCNgLgjqoZONBQIWEbAy4tg8CRE8+t4zy3XNL+H6XQqVYonzHUsFjYYtWypo9yqWdNitQQ7eFzDhkXCcHMdxrE+MDZNMmuEnD//Txhu7XN+8IHr1uXjHYVh9bXZ4yQYnPgAyjRffOeumiHx7JkrBO/8NevvXe5o/foD+D3/KlHCyRVhZvHNlSvDqWPHtXACeoFP9XObELJBd8aM1XAmLMPCJQxp0vFGFHQIsaZoKbrrzz8JThKdMbZ9e9NMFJsHQ4X+/SMwvoWSA0ROfeM6YWGR1KJFX2S83QLdGhuhjWvoPmfKxALYsXDWhMHAvBr3hkNYpP0iXfPmW4itthBg50bbtnWkP1t11exncevTp3fAAYIwWTvLuXOETEDzncB/hqhGbzxz2ihyfnBvzNueOXM6VRlQ+tHwb65Ll1Ho4wR+D9E2Iyt1x2W9rlgqXvwpnqU/IitkGrHzJaWUsWMXwgHyRJXzgzHo1SsGjtX9tH//x8ggUqYJklIwFOchEBAIJC4Cq1evRIDJS9XODx4dZ/k1bBiKbNgloPsTlL2J+42J3u1BIF26dFgbrEQm+gkEWI6H5tYhUJwS5i7x0ClzQfZ5PMXEpEb2azMEbHVBIJJRJJI9B3/H21avXgMBL4Vp06Z/MSfGpFVFmTzZk2rWbG7i/FDRlcUmly5dAnOHdvHtefO60rVr1xCMW8DiMcUOgYCzIaCdpc3ZzlyMVyCQyAhwBgjznTNlhprCUZeY41gsrDXSrh3hYWqxisUdbdrEgrrnNjINlpitw7QiSnlGzXXEQSdFirhINCXm9ottCREoVCg7Xb6sbmKVsCfdpytX4qBfk8XcLrFNAQIsOt2//2Rk59jW4jHuNnVqotmzI0AFtxyZV0gJsVI4k6Jz51F05MgiRPWHwrFg7Px425gFiFu1IgjUEYTu2YBM0Cd4u1/uO6b4CwwMQ1bJDblN3tRjx2bTpr2RbXAVTg3Lzo83DfCGnbBDh0Yhc+0mdIl+IKZzEsUxEahSpQgccOozSAzPatcuduobbtG958yQoCAvOD4+Am3VJ6YVkmDLypW7kEFySrbzw3BITIcwcGAIxEVHSvQphvuc9T1nFi1evAl0D9GqT4GjUkeODKNRo2ar7kM0FAgIBAQC1hBYvHganBYqF1kGHbdpE4l73gyDLeKtQMBxESgN8bOpUxfR4cNX4BDZhznUGmRVr6W1a08g8v88skSGC+eHxl8fB9uMHz+Xhg9PB0pi5Z1v3OiK9Vo26t17mPLGClq8RHRsQICKxaCFYwQExBP3KYpAICUhIBwgKenbFOfiUAiw8b9sWVL1oOQTYe2Nq1fNnxIbPJlep21b8/vlbO3Xj40c6+j+/YRpJi9ehIMaK94krVZOn+bqFCoUZdPwa67du7jN3d0NhsBMMEbbf/bQrwEXflZydRW3eXvRHDr0VzgYQqVIKzV9sbPil1/CYFAcZ7X5oEGzoFlwBAubSNlCnn5+uiwQFmkenZBS1uqxDHdWrhwBbYbzhptkvR83bhEcpbfgiI2VVd+wUvPmrxFhfh+LiTmGm8V7B0IgV65MuA4zqn6G6U8lFpfHqlUEZ5l+iy5r6Z9/OEvQF0LmzcHh3l0VddXbHtW9Yw2VYcNm4fcZLivzw9xR6taNB043oH1z0Nxup9u2atVuaH/EQDfGvqGXLElYON8H3/UD+zoSrQUCAgGBgBECLEz88uUTypHDaIeKjxwoHxHxnJhSSxSBgDMhkAnp1SUQsVi0aDFooYmM/8T87jJnzozMm3XUqVNGsAHIW1uzLtq0aR6gA80Fqr7NmFfZObGycYKeWHBGRsobm42upN2RkS4IXNMmEErO8UQdgUBSIKDdLyQpRiuOIRBwMgSKgflBrTGbHShsODJX5sBmyALrCEhQXTgSu127KDzM/0rQR3T0Kzzs7Og4QW8c8R0noryNMLH2sVOnlkiz9bZWRda+337zBq3LF7LqikqWEWCH4J49xyCKbF9ETYUKhEnpA7p48bbZgx0+fJ527/4bUdNRin/XfB9g58fu3bpsELMHsLKxSJFY3KeUOUBYIHnlyg2INFMfJd6jRwyE4ndC+PqOldGJXcmJQJ8+34BmwVdVdpF+3JMmkSQcyYKKGzcSsgI8qHp1P/Agl8bfBDgXv0gW5wePb8WKXchgiqb06fWjVffavXsEqOsWqWvsYK327j2EzFL1v2vD06lWLZoOHDhnuEm8FwgIBJIYAab5O3LkIv38M9Nd9oZGYVdq0CAIUePjQZWzC9pizmf4v3r1CrSXtDNjMG0MC02LIhAQCAgELCFQqFBhWrNmLzQNP4DmYQD0MXQBPcb1We9j3ToXzC8D6d695giQ2Yt5pp0TTeODmPnMVFUXLpjZoXITs1LkzZtPZWvRTCDgmAhoN3NwzPMToxIIJCsCHJ3NgmRqysOHZNYow9EER44QVaumpteEbZo3j8WDfGeCjX5+Xoiqss/Ya9hhSEgqpGP6Gm4S760g0LBhJVCGpZW+YyvVrO46dIjABZsei9xyVuuJnbYR2L79GKLUY6BhY7uurRpNm4ZhQoywdzPlp58mI706TPVxeHwTJhAyKsx0bmMTZ6hwNKWSMnv2amiHRNgVJc5ZK//7XxjNmrVSyaFF3SREoGrVkqAxrEBTpuDLUlE4W3HJEh9kNlVGxmEl0DrWp1KletKGDbNgeBsNXRANwndVjEvfZNWqTaCai9J/VP1apAhRSMhjaDg5P1XA6dPXiYM3tCglSsTQqVOntehK9CEQSDIEmI6SHfMrVvxDv/66HoLCmxEIEYy5cXiSjUGrA23adJgqV/4awRUDMBdfTN27ByMi+Tqy3q7QBx9sp5MnJ4GesA10eyaDCtB+Oimtxm2rn/DwCGSra7dW4b4i1HIW2xqs2C8QEAikGAQyQnhl3ry1CERdCmrkGlShQhpq3jwdHMppqEePNMigTUdVqqQFDXpzmjx5M+69U8jb2/7ARjkAchZQfLw3PXsmp7b1Og+QvOvpGYA/kQFiHSmx19kQ0MCk42ynLMYrEEg6BHgurfaZxxRGpUqZjvXKFYIYFYHayHSf0i2cLRsXF4XI9GhkaujSMvn19evUksiyFs+84OBUED/Oo3Ro72x9N5Cnz549EBoJP9Dq1WGKaZd4wvLDD35YuA8EjRmI2EWxC4Hjx4MRva7MOWDpgGXLxtOkScEmu3X6G0+Rwm6yS9GG4sUJv12C4UZ3j5Db+PlzgmEkQG51qd769f+AzxYHs7M0bBgPQ8whOH/s7Eg0TzQExo7tgQyou7ifXAfXdIzsDCV2xPbsGYisj/GI1HVMMc6rV+9Lz1MtwCtTJh4OnitUq1YZLbpLtj4iIqJlU/DZGmRgINGLF47tFOLoeM5o4+/uwYNnmFu5UO7cmUHNlo/SpcMkSZR3BoGQkDA4PP6E03YLqJVeg1ommjJkiMY17EoHD3rDeeCCaNhcMHy1po8+KuHQuDC9X1DQz6C5PYlM73CzVFE5cxJExKOlTM7587fThx8eRkDCQKpYsbBDnxsPztvbCw4bDRZC/51pWJhrkhkpHR5cMUCBgEDAKgKsCVKpUmX8/Qk7ShyCDq/Tw4cPJNrpHDlyQpg+c7JlNjdr1g4BR2Pp22/tW7suXOiJjMHOVnEQOwUCzoiAdjMHZzx7MWaBQCIjwEJZHBmqtGA9DmoYQgSBactbtwiLc9PtarfkzOkqLf4N21eqVBTit4Zb1L1n3ax791wpZ8731HXwjrYqUiQnaGJ+RESJL7HDS25hw3eLFr4wKPeChkx2uc1EPSsI3Lx5R7PfW65cRLduPTI52rZt/yJiSGWqmFFvjRoR7UyY1GVUw/RjcLAnnDzyQ76fPw9FRFAs+fiY9qV0C4vEv/dePO4TT5U2FfWTCAFPz9Rwxo6FlkNF+vxzH4vaVPrh8H1/yBBP/GVDhuEEh3V+6Bz/8ZoEE/C558kTjkXwfT0MTvvKWlRM36BFYUp9R40eZAPxjBmrqXz5NtS+fRCoLMYiY3c6PX8+DfQWI6l+/Q7IovwfsvYOpBiBey2+05Tax+bNh+HU6Ij7wQrasuU5LVv2kvr3j6ZvviEE8cQheCEMWSChyFo8jfnZUGRADodjxDEzQqKjY6C51Bv3pCM4D/POD8PvkbMxO3aMpQULnoE6dRCElB0/aytfvvwI9tAuA+T8+TjMmwsawiLeCwQEAgIBmwiw1mbevHlhs3lfcopkyZIl2ZwfPNgvv2wPB4gPMpJtDt1iBWYh+fNPX8z5v7RYR+wQCDgrAiIDxFm/OTFuh0cAAQF09Kg6YeK//yZiY6k5ukh2jmiR/fEWwHgs6k5jkb8H0VQvpQioQoUKwTBwDIt/+xZ3S5e64UFcN1knAm/P07ne1a9fidKkGUZffz0SC9mXeH2FKH3z5/DiBYGiIRWtXRtAM2f+5BTRe+bPxPG28u/NHq0dwzPi3y1HGxuXEydOwfhgut24npzPLDw8d66cmro6PJx//kmFrCH56Scsaoy5vmYlb954unbtPmXJkk6zPkVH2iLATpBZs35C1s8RRD/PRsZgCDIdwhEhHYf7FEkZg+cg9XDokC8dO5YKBuVG0KT5HJRujpuFFhcXj6wW7XDivrhPZy8FC2YFh/RT4owye8u5c24IAnG8aHLWQ+jadSR9/PELWr48Gk5Y4zPVzX3OnQsFhcU4PF9zITPgJ3GPMobJCT+z04Id7hzBmzVrOtAoeeO7XY3rYDF0rcKxzfJJ8VyA9by43oIFhzFHDqL16yciSwSpTg5UBg78lcqWvYZ7dYyiUeXJQzCchSKQZgQCKWY53HkZngyLCQcEpEcA13PKbme8z717nFGSBs81L2gvaudUMRyveC8QEAgIBJICgTSYlP/wwwhkYPehP/4IVWwzYiaB7t39of83jphSSxSBQEpDQDhAUto3Ks7HYRBgQeLSpUkxlQTTZvXv74FIAjakmC5eQD1Jd+9qd5rnz78EzcNvWCxFSAu/cKz7z5/3AhVEDLQPiAYOJHr/feXH4+iBefN8YQRroryxaCEhULlyEXCIzqGpU1fgu9gEQ1IcaNHCkVHzGoZ0ops33cHf7A3hRjdq0+YT1G32hspMQKgNAlmyvIcFNtlNT8Wj4ewtc0b+R4+eU6ZM2owXWdf0yDTJxGLn//5LoMbIJY1L7sL/9es4xRNqiwPADje3eFB3YcYtisMjUKdOeRj9ytOZMzfgOD+JCLHTiIIOhRZMavxGClLr1sXo999LOAX9nre3h2qNLnNf1L17nuDUhzfIyUvFimXxLDkLB4j9v8mDB31o2DAVabCJiOHGjQdp8ODxCPIIs5mhyxm8s2ZF0ObN5yFmGoTrfTzly5clEUcnuk4MBK5evQeNrFXQHjpEMTGxkvODnRn8LOPi6RmJbLVYq84Pw3Fx27ZtY+EgeIgI2T60Y8cMh3H2snPv0KE9cMyYrh8Mz8HS+xw5iH78MZS+/34cLVo0wlI1h9jeqtX/YOD7iQYNwsLJjrJggRdxX6IIBAQCAoGUgEDTpi0RhLsPTpB1oBgOlx3sw9m/333ni2y4ZvTJJ6AUEEUgkAIREA6QFPililNKfgT4ATJ4MCEaX9lY2AbYo4c3dejQjBYu3ADKkWfEmSCGpTCCKTnSVovCusec+j5pkvHiIRJGC6Ljx9kZw1GtOjoujg5jXRIWSLWWhcLnHxTkQ0OHdhMc2nZ+Ud7entSnTxssSFtBTPYaoqsvIjr3rrSAz549Kw0YUAjfSV58H64ics1OrM01L1u2FLjhd0lC6Ob2K9l28iTB0WgaVs2c8/wb06LwPURuVDv/TgcO9MV9ShnHa8aMaeAghQVIo/LggSu0bpzfcKwRHE7RTbFiufAcyIWxNobhz1W6H3F2k1wnmiOcJEeAZ8qUBtHgoXAA2j+ikydTYeGY3/6OkrmHL774GAvfVdS5cyi+V/WDuXOHnbHeyBLKrb4TjVueOHEZxtLxtGpVGIzX8juvVy8e9Z/D2N0LQR2zEXmuAf+f/MOLmioRiIiIQiTsLFD47ZXoqixlaPF8tk4dphBl+j6CQ1feAevXj6MjRx7CubKYevduI69RItcaO3YOrvEw2fMAc8Np0iQOjr+LoPR7AApQjaIzzB3Izm2ffdYcWVxjqF27CLMaJ3K65/vUX3/5IePlSznVRR2BgEBAIODwCPD8dvToaaBr9KcGDZbCzvOCQO5htZw9y5kfAXgWtsNzE0YsUQQCKRQBTPlEEQgIBCwh8PixpT3Wt48Zo6ME+eUXQkQsgRNS91exIlHVqgRhKh1Pv6HRk2mMOnb0xiS+Ojz2X2BB1UtyIrCTwrDwwowjtFgk3d6yaZNuPJb6KVOGiOt8+qluvJzVMW4cQSiRED1JZDw27of539u188Z5NoRw7keWuhbbFSLAguZlyuQHH/UncE51hnOpE66XBtI2IXauEEwF1WvVKo3oXw9NHBRr1viBVx4/HqOSJ092RVovRs0TfGTNGLkaQSNHelDNmjWpXLmCCfqw9SF79gwwGsdrgglnMl25Eg8h6qy2Div2CwQ0R6Bu3Y9A/+hud78PHhCFhHiAikWBVd3uoyZOB5kypUX2aglQAtnHDzZ6tDfmOq0k51jijFRZrzExr6BvMJKmTVPm/NAfpVw5QnDKCxi6J+s3iVcHRoAF7cuU6QpKp7+h6xKG5xUeNhYKz8VDQgi0Vro58bNnFiqa2dynTxTarXcIPZAnT16ATvI6aFDNDFTBJnZ8tmkTTvPn/6WgVdJX9fb2ppEjZ0KjJcDsesTWiGKQJNOtmz+NGDEd2fq+tqqL/QIBgYBAwCkQ4ICkEydOIAg1CwUGFoGmqCdsOamQxUp4RhCClXR/vGZcuZKwPw0CLQvQzz+vxusQh5m3OQXYYpBOh4D9qz6nO2UxYIGAfAQ4GmzJEpJNT8PGvEmTCOnwumMwT36zZoRUQkKUrG5bGLSO8UzCokwXaTZ0KNHDhy40ZYovsj/aQuuhgfTgqVGjNOiNmsKZsAqUIhEJBIc7dNBll/z6q/xzMa7JC75Zswj81sZ7En7myLigIAJPMhFrk8yfT1hMEjiTSaLImjKFJK5wjjzfsoWwGPFD6jyfxycJOxKfBAJOiABzexcvXphYqJwp4dSW8+eJwsMDYVjMZ9IFZ5kcPbqfqlVDSoadhXWHWAfEWuGJLzs/7twpCoHULtaqmt3HkUVlyxaEaPARyaFrtpLMjTzeggVzIlrVPmOrzMOJaomAAC+0mBLr0KFzyE68AAHtV9AFSYPMtKKgciwCLauARDiqNl22a/cJtEzWwbAdSqlTq+9zzhwP9NE0xSwaf/65B37bHfH9vVAVWb1hgws9e5aDvviilnpQNW65ZMkO0Hk+t0vbhGmP6tU7jsCQw8TG5qtXb0j0fRkzZoCxvRDui/kFDaXG35ua7sLCInH99gRN6EOrjg/jviMjCd8p4TtmbSymxjKuYfqZ6zRrFg26qK0wpjcxrZCEW5j+qnLl17gP2X/QqlXjsCbBYsXBS7Vq1en06a5YK83AWullgrWStaEz3XCnTv4Qvv8GWSS1rVUV+wQCAgGBgFMgwPPxNWtWINBjJGXLFobn4Avcx+Og5aHTpV21imjMGDcEq8bD6esHPcc8mKu/D7aPZlg72lg8OgUCYpACAdsIaDBFsn0QUUMg4KwIIMshfsgQQnQQIZvBOu0T6wR06qRbPH38MRFnf/jYYElgL3y7doQHUw5QXo1A9GhGE6h+/30dTZ++CGMIxURdJ8jMjhYeD2eS1Khh0kTWBhZKPn2aaOJEWdWlSh076pwe7NThcuaMbvylSqWGbogHxCFLgJKpEzQq3tNVEP8THQFnpZ9JdGA0PAAbURo2DIKDL1SaRCrtmh0On3ziCx7W4WYF6tmQVqtWR2gqKBerMxwLOzWrVCFkrJAkTG24j9/zfYMdDgMG+GJSXEPKJPL0TCUZbXnSrIS+6ODBc0itHkRLl8Kja0fp2NEXmUz9cf5l7OhFNE0OBPiaYaPfjBnLQA8UgWs7Ain2sZIjgSOoT53yAl2QyoiPDwAAQABJREFUu6QN0rdvR7zmSo5h2jzm8OG/Q8R9PfXrZ5RuabOlrgJTUnbrlpH27ftd0kKR2czhq+3dexqUXkMRrBGmyAmyY4cLDR+eHk6CKRAXT2PzPJPqGfbBB+3pt9/u2S2YzNGSEyakkoJb8uZ9JVENPXzoiuvdh/791wV0Ex8ggrINaP0CbZ67qGA/Auaun3btfgHN2V7ofagLKmCnxldf6ebxckZ48SKvEwrD8KRgQi2nY4V1xo5dgmClBdLYFTY1qc7zhUqV/Ons2RUm+5RuiIoqjPmFvzQHUdpWbv3582fBAfIzgjuev1krmWvL57V/P1HfvoFYf/0Ax3WQ09I4mjs/sS3pETB3D0r6UYgjOjMCWlxDIUhjDAr6iry9T4PK8aVVTSvOWh461B+BKoVgY1qCTJF0SQpfunRphA06SREXBzNEQFx8hmiI9wIBIwR++IHiEcgK475uwswLIk4tzw+ab44WZUoopqJat44koWROp0Yb8EQbdWTlI2dOcEZHzpx1YSDtYbbmlSv3oAMxEeLnN+D4CEekYZyk3dG1qy5DJV8+s80sbjx4kEDjQLRxIztfLFYz2fHkCVGjRoTI77fOIBZ2/uwzL6RVTgAnfG6TNmJD4iKgxaQpcUeYMnqfP38jrVgxF1QX4bL5wfnM2Snx449eyCKrA4dDF4tgdOw4DNRyB6lpU6zOVZYVK1zglEiN36EHDBfhVLjwa0QjEya4RMHBHrRrV2rKnDkHIn06vaG9Unv9sPG7efO++O2fRlo1TlJF2b7dBQaLAhCmnZRiIudVwOCUTe7efYLn1lAYk+/g+o60uNBiYxNHUg8Z4ovr5VPq1au1w33XbCStWfN/CCi4C4o6Zdcy02R+/rkvMiJHvvlNOeUXamHQ7AT53/9GwhESSi1bWo8s54jqMWM84WTNCPqs0bjXyFtQq70HWRiy2c3PnoXCMdGBtm9/aXa/ko18npwNuGePaSumBV21yhXRl76SBlqTJlVNK4ktmiJgfP0EB19DRH9vifbKngMFwn/F2dysfWer8Dy+SpUAOAuW26qaqPuHDJmN9clqaZ6uxYGqVPHH3EG9A8TNLQyRxtUwFFc4P1wS1QHC53sNUWVDhnyHtdI5rJVCsVaKlZy3nBHDa5Xjx93gpPKHY7YQdBonAyss5lCMryFpo/gnEJCJgLh+ZAIlqllEwN5riJ0fzZrVRCDuTWhZ4YEks6xZ4wYGkmygwfo7SZ0gwgEi8wsS1RIFAeEASRRYRacpBQEIRsVv367L5AgOJtA56PiBWRycF8E8d65QQbdt9WqSIgs5K0Np4cVTvXoumJAPg/hUeYvNb916BGPhAQhhn4DD5SEWFlFwwjzBwyuWqle32OzNDjZGcfoj014xtRdriSgt3boRjLQJM0/YkDlnTkFav36iwxm3lJ6fs9W3d9LkbOebnOOdOHEprvFViJYJlxVFzFRxP/zgDedHFcn5sXTpdhiDD8KhehuOkTjJGJAjR3o4VUvhd/8B9F0Gw5n6EpHDys+SnbGNGweApu5X6HM8QyTyeRznAnixo8H/GoDU5mKgfymKlOiEGgX2XD8PHz6HoakrnBghVKSIsjEzzchXX/kjSnw6sEw4JmU9idpJjcCNGw9wrfXE84q/f3kOu6goQoaFFwxNFZAx0tfhnhOPH4cgy+t7GPkfU/v2scTUj7YKZ1AGBfnit/0DjOuVbVV32v3sPOjXbwrEnk/CiRWJ+0gsnKs6oWjW/OIgkB07vGjr1lSgvmwECsyWiijt7LkHyQV1584TcGCPhIaZfRlr+uNxNi5TfkKCwGxhx1inTj4wRLfENfK52Tpio30IvMbEOSYmFpnWHtL1ps9ibNVqFIw5ZrxTCg/HRvM2beRnSVeu7IesaqQHJWNxtAyQ8PByQCNpnB+GsN+9ewf3o82gEd4NZ8gtaa6VNWsOzIM+xFyrPuYcCRc/SXEPMhyfeJ+yEBDXT8r6PpPjbOy5hvjZ9+WX9WCbOYqAHPnOD/15rlvnSvPmlUAg684km5sLB4geffGaHAgIB0hyoC6O6TQIQAQ8Xq/nwYNeuFCnfcHi4Kz1oaetZ4dI//46wXA5hhNzANy5wzQ5XuBQX4HsklTmqpjdtmHDQURoDgelTZyk1VG0qGk1dnwcOqRzfLCWB4u0q812/OsvnYbJ4MEJj/PVV77IUhkG/mGFltCE3YhPChGwZ9Kk8FCiOhDYuvUIotgnwAAcDuNIjFl9oBcvCNRQbtDL8YExsA1dv36H1q79W+IJr107BjRBJGVw8e/y5k2iAwdcQCXki0V6GugnPIHxJsIshZWlL4AzPFq08KGBA3shgr2SpWpmt9t7/XC0batW/UA78UI2HR/TT/Tq5U9z5w5PkVHzZoFOIRujo2NAL9IJdC8PcK9XdlJ8vffs6QXNl2bIGmmlrHES1A4NjcAzbApdvHgUv9swiUbF3POcI4l//90TNCqByPwYYFbXJwmGm+SHePQoBJpBO+AIOY55yi0Yn18jg9QTRsV8yF6rhGywD6GZgNRYhcXee5Ccwy1btgsO4Ym47yBNV4Py2WeELA+ymPnEh2CnX/PmPnCC/5iiHWQawCmrCzby/PvvBfrjj9W4Bs/jWRktaXRERsbDCeKDDIwScMA1odq1+9DTp/DMaVAywDfP9FZySqVKfqCFTV4HyJYtR5DZPZpGj0aElp2FaX2//z4PHH0zVPfEDpD4eDfJAaG6kyRomBT3oCQ4DXGIZEJAXD/JBHwKOqw919Dy5QtBN/sT5qOhqhHp3t0X69lvEJznBjrXffTgwX3JGZI1a1b64IMPMb9r8iZjTvVBDBoKB4gBGOJtkiMgHCBJDrk4oDMhAK2L+Nmz34542zaiqVMJxsy3zg/e+803Or7gqlXf1lXz7rvvOLPiR0RG10rQnBdzS5Zsg8HlX7pw4Y4kuunq6oaHUWZEON3AmMIpFM+9OXN0tFzFixMivXX0O9evExaLBKErNjoSojcTdK34Axtse/YkRAokbMo0JytWVIDRd1jCHeJToiJgz6QpUQeWgjtnQ+miRdtwra/BZDEC2gZEAQFxyApzAQWGK8TO3aVonFq1KlLXrqPglHgGCpkYyelhDZatW11ATech0VZNnhwl0e1Zq8/72LHZq5cfnB9BoHxQfgPS4vphjZTOnUdg8nwXWggRUnS4uXFfvkyI/veGyGxGmj17EOXLl8VcNbHNgREYMGAmHPSb8QxQZ0hmkeG6dX2hKzERjsDsDnmmLCQ8ffpiGFvP4bftiudstBTs8OhRKmQ7uCKLxQ/P/M+REVpTUbCCQ56sAwxKi3uQrdPgjICTJ8er1nkx7v/TTwn3MDLrADese+8eC2QHIvPvNzi1EX0iiioELl26I1Gx+fo+gW5DqOR8NdTY40yk3buJZs70oRMnoiTnnKoDGTVKm5ZwHyDiV2slDIlFn3ySAfRviJJKxsJrhTp1OoDuMhTGK/sGsmiRKzLMP0OmHxY4KotwgKgETjRzKgSS4hnmVICIwSpGQO01xIEBVasWo9Wr70GLT/FhpQYnThDWqkRXrrhgTYtIJaPigodJIDghc4A2ZODAQVjjRtDjx48wF3anXLlyIwimBIJhAoxaWf8oHCDW8RF7ExcBO6dHiTs40btAILkRaNCA4jnrgwtH8zHNFFNIwSH+pvB2joTlLBB7Fxwspvrttxnp8OEFUv9saO3adTQ+n4BTJBaRbfQmepyFlTkybdcuAt82UfnyzLPOXLYEIyzBe68bD9NcjR9PiGwlxTQ1b07S4E14OIEqhGjnToONeMs0Xu+/H4AIOAxGlCRDQO2kKckGmMIP9PJlOK75G6CjiwAdigd+YzkpffoAunPnMQwi3Wns2GeKIuWZOqVxY6YK8oWDIBLGnjDJEcKaQ/rCWkOHDxMiYf0wCU0rRaEXLKjOmKzV9cOT8LVr92MsS8C9/hgT4njoGkVJ98Tbtz1hlHJBlG5a6tKlJQyCVUEx5Ko/HfHqJAg8fx6KhVZ7aB+ESs8ZtcNmWskNGyrDYT9YbRdJ0u7Vq1g8S29iUXgXz7c4SdS6VKm8wpCtMfpa3YOsDevEicvIfP0J1xws5RoU1oJjLTU5t7Hp091wH2wJ53ZrDY787nWxatUuZBjOoF9+0Tk+rCHA2YUtWxIMNNZqyd/Hjg8O9ilZ0nob1sVbvboy/fZb8t/TWrToQx07nlI07zA+OzzOsd7wQybrVBi4Mhnvlv1ZOEBkQyUqOjECSfEMc2J4xNBlIKD2GtqPh96CBa0RkAHOZYWF7/NDhxLaE0FCRFZhZ4i7eyqKjX0lreP8QCvC2/IiyrZPn74I4q0pqx/hAJEFk6iUSAjAVCqKQEAgYAmBkBAv7ELIKgpTP338cULnB29np0XBgvY7P7gvpsa5ffsx0sXjsejaSz16jIFDJA5R06YLbXZ0cOQ5/7EuBz/AWJiT67IzxLB4eBAi4gy3qH/P/aRKZdqe6cDSpo0npsnImDHQtILYIhBIgQj4+zP1Bn6EBoX1Pdq3HwqHpDLnB3fBETxr10bCyegOQ04PODe3YVJ5GVQfzHXO2SVx0PVwB+1OfmSVNJd0PXjymVSF702HD58HzcZhpEmfkahGUqVyR9ZHTqpVqzTG+4s0GT5x4gpERx9hWPEwxGQAJUc+YThOqi8pkY6zePFWUPpE2eX84KHVxPpo6NAzWHCFIarMN5FGa3+3fF2zw4P/RHFuBIoWzQVHtU53yd7bJdOVsk6THOcHo/bVV6+h8bYJmXpfSfdG50YyaUe/bt0+mjhxGubf4bJoIdmgY25+as+ouU9bZfFiX9BvITLIAUqvXu1BXfUT9MrCVN+r//yTM8wLkz3ODweAQgxBICAQEAikaAQOHtxF1aqpc3706EG0Zg0RZzDKLbwGfPVKZ1BiHS4WX+dy9OhR6tChA5UBR/ysWbMRCJhebpeinkAgyREQDpAkh1wc0JkQePDAXeKu5QXzihVEo0aZjv7CBaISJUy3q9nCC2p//3jQi0yFA2STFHlmTtPDuG8eX9u2BEOsTrRx1qyEY8qXT5ctUqqUcUvlnznrhMXfzZV06VwQ/R0qHCDmwBHb3hkEFizYguyHe3AIqDtldoIMGhQGvv0NcCiMkToJD48CxVYUskw84QjxVNexna3Wrz8IPvtfpWwXFkY2LKdPX4eR6gCcNb/DEVuOJk/uhkm5jbBZww7Ee4dH4J9/DsGI+8rucfJz7v334yQ+/9q1y9ndn+hAIGALAdZVK1u2MLKXjiCLyVZt6/sXLSKIjVqvY7g3IIAoS5bX0IJ6QHnyZDbcJd5bQeD+/aeg25gCQ7485wd3lQnJCnptPitdK9rFfVorTO148aI3MsQ1mGBbO5DMfeXKFcT9tSqNG7ed+vZVHvnENLcTJvghy/sHmUcU1QQCAgGBgEAgORA4e/YQaJaVH5ltWkznrsT5YesoL1++oL179+BZWA3aUVvAlgIudlEEAg6IAJahoggEBAKWEOAo6wMHEMOMCDBeFJgz/N+9SxJnv6U+lG73QtLJ0qWboPmhy+5Q0p7HN3euLiMkIuJtSxZtZ8ocLQr3w/2ZK9HRJDjRzQEjtr0zCHB0zPTpy0B3YvADVHH29evHI4PiKt248UBqzU6PDBkCk8X5ERUVQ59/PgxZLeMwnodk7PzQnx7XYwco8+0XK9YRvPen9LvEawpA4OLFu1SggDYnUqJEODQZED0gikAgiRAICmoFOlAfcFyrP+CzZ7qISejDKSolS8bger+qqM27Xrl370nUv38opUsnH4k8eXTad/JbWK/JwUXWHCBMRdujhy8c/n0citZx2LAuFBycjyZNSq1IgPzKFaJWrfwQwTtIovK0jo7YKxAQCAgEBALJiUBYWCg0KJWN4PlzogEDSNKOVdbSdm3OCrl37y5YDD6hSBb9E0Ug4IAICAeIA34pYkiOg0DXrl+A094XNC86ahpeDBkX5ua/f994q/rPLOZYpw6BdkNdH4ULE8RZCdoDb9tzxCNzI9vr6WfDwcqVRI0ave3b8N2tW3GUPTvC10URCLyjCLCRK1u2KMqsQaBvq1bhcIZuTVYkY2JegfqvN23depRevAiXNZbY2NcSFV7z5sNp+/ZjstqISs6AQJxqShXjs2NufX3qvPE+8VkgoCUC7JQdO3Yxdeo0jO7ejZbEy9X0z4EwvXsTsuCIfBUyt6VLp3MOqznuu9iGsz/Onz9PDRoAdAWFs8tKl1bQwEbVDz+0XIGdH926eeP5WM+EBtNyq6TZwxlPK1eOgRZZJdAW+iBwwfpxmdp21qxU0BxLBx2TEaCtLGK9gdgrEBAICAQEAsmOgKenl2Lbzpw5ieP8MATjAYRo+/fvZ7hJvBcIOAwCwgHiMF+FGIgjIvDBB8UQPZUTRjwi1tEwV5ii6phGNr7QUIKwFFFrO7UyO3Yk2rTprRAkcyK3aKHTBzF3DnK3sfODsz/MReQxL3b69IEwkEEMRBSBwDuKwJEjFyBaLs9RYAuiSpWYJuikrWqJup8pr06dukac3aG0PH8eRm3a/AwHMTzIojg9AuwAZyOwFoUDwzwsPVS1OIDoQyAABNau3UsffdQRIvZLacOGZ7R7d6wUxMHzIyWFr/sRIzjDlZANp6Slrm5MjCu0KdyVN3xHW6xevYeaNYuEZopyAH78kRRHxJo7SmAgQUvD3B4CnRnToPkiK7we/fRTe/OVknmrh0dqBHD1p+7d+8D5lwWBUX40f74LMpEIjhGia9eINm8maJV50Icf+tHjx7Wh6/U7NAQLJvPIxeEFAgIBgYBzI3AfkbHTp09A5nxD3F+LwKlckGrXLoMAivaYl6yGLqvyNZU5RAoXLitp0ZrbZ2nbvHna6cJaOkY0KEH++msdskHuWaoitgsEkg0BMRtPNujFgZ0BARYXnjGjPx5aXWHYR2qGmcK6G7yY4CwRc44BM00sbtq2TbfLXk0RXqQ3aKBb3OipGlgonUXSObukZEmLQ7C4g6m+Jk7UOVbMVVq40ANin5+a2yW2CQTeGQQuQBToo49ea3K+TGl39aqOAkuTDhV2cuzYJWiQ/KPK+aE/FEdfsxNk+/Zf9JvEq5MikDlzGkTQv0CGk/0ncOGCB55DuMBFEQgkEgKjR/8BGr4NEPkMT0BjtHw50/wQnThB0LTROTWsDeHxYyI2qvv5EU2ZQqqM8hcueGMemd3aYcQ+AwSOHj0hiccbbJL9tmJFXQb1vn0Ex5fsZgkqso5I+fJExYu/3czBScHBRAsXekPw1Qvz4d5OoXNVt24FzP0r0PHjl6HrcZRmzgwGVeVLySFXsGAeqlChFA0dWjFZ6DXfoiveCQQEAgIB50fg0aNH0K76li5d+hdO5zA40WOpUCHdPIOpp4KDr0MfYzuNHt0bouHdqWPHbnbRJ5YvXxX9LaImTczbqIwRffGCQE1lvDVxPoeBdmTWrF/xfBmWOAcQvQoEVCIgHCAqgRPN3h0EsmRJRwsWjASf4Xd4aMSb6H14exMWDoTIKoJ4uXpcOMJw9mxdpgn3aW+pXJmw2CFE0el64mDbGTMI0Qg6nRCmypJbmOLryy+JfoENk6lLjAue9/D0e9KhQx8b7xKfBQJvEGBqpM2b/4VR6pDE/x8SEoGIWndMDrNjEV4W12o1p+edZsoorQLbdWKuGoXcv/kW5L8ZOHAeaIrC5DcwUzMuLp5YIP3ixdtUsKAwAJqByGk2lS9fHPf4m/id2n9NHj7sgYWfiDR2mi/fyQb6++9/4VpdDwrBCBh6Ew4+fXpCNoguoKNaNYKhnahxYxYqf1uPs50uXiRavJikDOC+fYk+++ztfiXveG538mQcjOm5lTR7p+teuXJHtd4QZ43wXJrpq3huqqZ4e3tC6yoNsjnDQHfmQq9exePPjYoWzYm5cGM4ESo4XbZzmTL5kcHNTucv1EAi2ggENEPgLiLqFi1aQLt27Ya+5g3oMjG9pjt+8wURpFcXhuMvkMUVoNnxREcCgaRAYNeuHaB96kJ9+jyH4T/OJFgiTRoipiSvWjUM+hthNGzYL5hXrIJ262p6770MqoZYvXoNOFxSS3oeHKRhq4BZMslKLKIGNm3aKBwgSYa4OJBcBIQDRC5Sot47jQAvGmrXrkQ7dhyEI8QUig4ddAuupk2JcuY03S9nC4uec7RakyZyatuuww6OmTMT1uNtvDBkJ0jnzkRt29qOZmSqiGFw3o8eTYhsT9gff2JDQc+ePnjAdRMRZKbwiC1AgIXB2SA1ZcpSpAHHYIETIV1/7ExDliy4vu/DWHWcatVaSJUqlcG19i2lSSNjJueA6AaCN4OFcrUorNnj6Yl0rmQonLnBEaNaFKbCmjZtHU2dGqRFd6KPZEKgdeuGFBS0Gw4QcDXaUS7jsoqPT0N58mS2oxfRVCBgHoFr1+7jXrMQUZGmzg99C3aKsJ4Hz4V47sXzITaWs7YHz8NAX42skXQUGPgSDvtXuA/rWyp//ecfpg4tjOAZD+WN39EWsbH26Q1lgC3pr7+IPkVS8pMn8oXR3RB18N57gQgeGifdn9gwGxkZAyeaG4I1jDxp7+h3I05bIKAWgaegSvjuuyA6fPgwDLahoHxGWpVBYd2Aw4cP0bhxv4BirplkOPW05+Zr0Ld4KxBITAT+/nsbaDI7IeP0hSwNSHZWjB0bhufUWdBq1qJ163YjwNRMhKmNQbPjsE2bLvjNTMTvJdxGbUJGv/znoc3OZFR4jrQXFkbnZ6soAgFHQcDVUQYixiEQcHQEevZsQ3Pm+JnlQG/TRseN/s036oTGz5whLNh1VFociahF4TmjOYpJTunfupUjEomqVyeck47PmKMUufArU3oxTUT9+kTLljGPtq6ursbb/+z86NXLC06fynDcmPGOvK3qlO9evgynK1fugYboHgz1MU55Dsk9aDakf/JJDyxq5sMgFUITJkRINGwsEs6ZEv7+hChLAk91LO3ZEwZD0X5Ex3yD98HJPXRVxy9VqihSnO2wlhkcle8LJUrkMdiSdG+PHbuMSSt+4BoUdoDt3o0bjihOjUDhwjnI2zsrvkv7TmPMGB8YQRB2L4pAIBEQGDJkBg0eHCpRVtnqnrNBvvuOaP16ooMHdUZz1kXYsoWDQ9xAbeoPDmtbvVjez3OkceN86dtvxfVuGSXTPRkyBNDDh6bblWwpUIDgvCIqUoTpR2zH+wUG+kLQvAzorWa8cc66QlXdx8dTOD+UAC/qCgTMILAPnHSVK1dCRt12ULA9N3F+6JuwdgDvX7SIA6IqQKvmmn6XeBUIOCQC7LgbMKAr7CXynB+GJ/Hpp6+h0XQbgaSIpFVZOnXqjiDCLNBwst0BB3kkpS/CxcWVwsMRzSeKQMCBEBAOEAf6MsRQHBsBpi/IkCE/rV5t+rNhTz5nSHA0e/PmpGjBzA8sdpww7VW+fOadFmqQ4Sh0SxnEbHSeNEkX+bhwoTuiH9/DxNQfFA2c4q+LhuQoXXbKLFjAkZCmI7hxgykhfBGpXwNcyD1NKzjplgsXboOzczz46b/EYrgdBMu+h3H+e1A0tcJkvC0iNhZBLDLESc8uaYf94kU4tGh64DdxBddbhMXrUT8q2BpAL/EazrcQ6tFjGAytp/S7nOb1/feLIq0/lVlHqdKT2LbNE86gKkqbaVL/1KmrxN+fVuXxYxDPiuL0CEyZ0heiv34wUKg7lT//dIVjLR81avSBug5EK4GAFQT42XzmzAU8u61UsrCLnz9MZ+rlRZQrF2fzRoKm7XM8/31Af2ShkY3NM2akolKlKgthaRs4Ge8uU6YondLg8f/eeyxW7kZduzbEs7QE5qu+oNn0R+CFt/TH73kbOz7WrBmCaNzhTk/DaYyl+CwQSG4EDhw4AMaB1nAoP5GiweWMJwqh6rdv30YgXj3QZN2U00TUEQgkCwL9+nUBDVWIWVuJnAG1bPkaAatHJLooOfWN63AWyOzZq5DV+p4UyGG83/Az65HoA14Ntyfe+3honIjsj8TDV/SsBgHbITFqehVtBAIpFIEpU3pTjRpd4CQIkUStDE+TRcePHyc6ckRHY8W0WKAxlSgVDOvp32NeR5MnE0EzmfLm9YAgZxyiAF5JIotlyuhrqX9lsUZDAUfjnvgBOH26J5UrVxFUWX0R7eiCh2I8OLN30KhRv1NYWAwM/ZFSKqdek4TFs/gcly/3oXPnvBDZ+APwKG3ctVN+5myP3r0n09mzJ/A9hMHIF29Ce/HsWSjOfRnVrLkO4mXNENX5uV3iZU4JlIJBBwWNodatH8AAAU4RBSV7dnbOhSEteBSoKGbB8RiooHXyVs2UKS0oNLIjEugclS2ro8RTownCInWbNqVGSnPVZDmhkBDtnB/JcgLioImCQO7cmRBd341atpyKZ0W4WU0oSwfevt0FTvX0iK7vLz1vLNUT2wUCahHYufMkBJ9jTLi31fTXqFEodEAegJqxAXXpsoF+/dUypZa5/lescIPmVRbcx7uZ2y22WUHg44+rYH68HXOHUCu15O3avNkbwuWNiO9drEN27txNunv3iXQPyp49g6RBJug55GEpagkElCIQEhICqsF20JNTFzj2+PEjrKVb0N69+wWNjlLwRf1ER+D69eugzAxGsJ/6jHnWrRo48CWyQIZTvXqg3lBRcuTIgXXzVvzWPoON4iGCNyNMbBjcLWeAcHCs2iAmpUNj25IPR5aIIhBwIASEA8SBvgwxFMdHgA2xCxaMQCRLf2Q9vJSoewxHPWAAQeeAkCVCMKQTHkI6J0TJkjqBTaY7vXKFkGJPkmBVnTrsUPBBhEsj8AwfxMLsupT2366dYa/q3iPLWBL3NNeaKa6+/94HosSV4QT5/o0xih9UX375MQzP1UF7tQ8CoDsh5nUN0Y+vJOeIj48XlS5dgL7+uh7os0qlGOP/9etspP8RE4cQZHjEWjSeMD1n166x4NsMpeHDl0E49TAM3aOQvaMN5ZG578oRtt28+RCOvYvQ6rgC/tAo8KIH4jooJEW1BgSYn9hs3HiIXr48h2tQmfNDf77ZshH9+GMoJnET8Jsbpt/skK/sONy79zQicJbDkXkZEaRxtGoV0caNOno5jihmfSB2iAI6WWX0aC/8zhonG298mjQ+0n2Bz02LwvcWUVIGAk2aVMO14Qo9rKnQhwrDc876NcKcw6NHe+IekhG/iZ9FhHXKuAwc8ixOnAgGjSJScTUopRHbsXTpGVyzUzEvcAeV41/IZAwj1lKzVkBtT0OGeNGtW9mRUTBaaKNZA8vCvipViiIL1At0rKHEcwG1hefaadJklZwf3Ie7u5tEK5lc1JJqz0O0Ewg4KwI9e34vUVrZM/47WLTOhKhlUFCQPd2ItgIBzRFYseIPrHMRHWpnyZ+faTcfEDtUsmfPqaq3nBCh3bLlMOxQY6DZOg/BINHIfAyXgmGZqYR1JZlaOWfOVHTjhsq0VoUjy5Url7SWVNhMVBcIJCoCwiKRqPCKzp0dgZiYLWYtO5cu3QE1wlAstB9Tt25RJoJXp08T+CB1QlOVK+tQYDsiUyyw950dIdu2segm/wR9kXnQmk6fvoZo9y2Sx37+fMIDUD16LObJ4o/Mf6yPPo+IICm7hLM3/v3XAwap7nC8QHzhHS9MmfHxx92QzfIUFFfKwPjtt1SgacoP6oRxyeYMcnd3fWOoZuFQLcv69Qdh8JmPlPVnwCYGhp8o6Xp69swF16s3HTjgigyiYrh+28OZlvCCrVmzC4xGN4h5uNUW/s3UquUHZ8IM/B4yqO0mUdvduvUI0cEj4Ki4Byq7MKoCxir+nRuWx48JzkQ2phGMOoToeZ7oGtZI+H7bNhdEv+bA/WBGoke8Wbp+du48gXGORNSeNtytRYrkgO7Q7IQnKj45NQJ87ffo8QtoLW7BcR6Ke0Q8DI2665+fN7zQYhq39etTUbt2jeB0/0IyQDr1SYvBa46ApXuQmgO1bTsA9+GjoJ1S0zphG9ZQq1UrPYJUFkk7OAjgu+/GwCAfJl3v5cqxcV3XhulPz51jDRFPKXMvKKglqEUbJ9u8IOGZOPans2dv0I4dR/B8OI058TPpHpE/fy68+mLbZlq5MsLq89LS2TFtWd26vsjcGY2ADViXRElSBNzcwhAEUwPBU5xdnqSHVnwwLe9Big+eghuw6HnFiuXtdoAwRO+Bz+7MmXMOeU8V108KvohtnNrnn1eD/eAU5cplo6KM3Rz4VqjQNARXNpFR23qVSNAIbNq0Efae7QjIPUkREZFYp3pCE6skKL4/opEjR+B5C2NRIhbO/Bg7dhy1aIFFr1FJly6NlVWwUWXxUSCgMQIiA0RjQEV37wYCBQpkA8//rzR37kYshFdBbPMFouGjJbFFFpe6e5ckRwbTUOmdHizoyIuArFkJDx+i8eNZnDEeBsZQ0NzMo7//jpV0OXjR3aePzmBqzUhqDekePVxBTZIV9FRhFBcXKx3XA54Qjnpr2rQ+FoTlE92wam18jrQvKOhnpJ0+V+z84HP45ptXdOnSdUQmrYEjDCH+KaQw1df//jcamT8X6ZdfwuHcMD4xXs2G49oiXLcH8Rs4jb8mwPELyRlz7dp9XHPP7HJ+8BH5+m/dOhxiiJuoX7+2xoNI9s/79p0GTsORDRRqNQo+A3w37Phoi1Pg3zbr/nCmmDkhuj//ZJqgDIg6HpOsv9GyZQvg3qXN/JT7qVGjTJJ8Xxcv3qZ589aBB/ckKPxeSPffuDgXcPMGwjlVFlleOiqUJBlMCj9IjhwZke04DhFr96Xf6IABpxBVxguqeEk0mHWzqlWrQoMGVU/xWXIp/Kt2mtPjew0/l7Qo3I/hPbB8+YJw+s/F31lQYW6C0eO8pJPEzyk3N3cqViwX1a5dFVlR1ZItc0+L806qPvbvPwPaj6lwdDwDfUg4no9xlDGjLkDo8uWLUqDOpUuvoRfkCgqrOJsaYobj5rl2v35e6LeucH4YApNE76OiCiNwBiHHorzTCKxcuQJR59oE0bA4+uHDh6BXiSgjUQQCDoLAnTt3kVGhzWCKFImEs+KYJg4QL1APNG3aTPozN7psSK38+uuv4aS2P3vFXP+8zRv86Y0bf2Zpt9guEEg2BIQDJNmgFwd2dgSYM7hjx4bIqrhA9+/vh46Hjt6Ko703bWIKBMKijWAIsn6mTIkzcWKERJs1YgTRn38S6KcIdDo6MXLrrU33/vEHTNPhuZD9MV0yRpvWEFv0CPz993EIj12iJk3UW0wGD45EBskyOABqIxrU+Rd8jx6FgOqjB7IaHoMK7bXVyEt27rHY7AcfhFHfvivhDLhJM2b0xSLlAozN4L3RoLz/fhx+SxCeIXgPHKgEB19DOv4wUKCFEqhXZRWOFp41S3dv6N1b5wTVN7x2jaC94w2nUz6kMP+U7DRBTG1WsWJhcNj/qx+i6tfAQF84CJGSlojl3r2n0OQZQ8+e3YAhLQz35nhEDOoOyMaw+/dfIlvrNlLVd1CePIVwz/0x2TFORDiStOv8+bPC6NsJTs94ZDeqv5cm6aDFwVIkArlz56Rr144gO9f+0+N7cu7c/91E/uuOqfzef7+Y9Gf/Ed7NHl6/fg3nxDTME/YiOjRMChwyRiJPHqI6daJRj+DQ5jkG0bRpBDoP45qmn9ne2revF4KQKiATu71pBbElURHgzI/Xr33wPHD8zI9EBUJ0jkDBXRKFshZQvIAI5cGDwgGiBZaiDy0RiLe6TlZyJKZLjorSxmFo67jVqlWn7t2/A9vAZDhBXtqqrni/v78/AijHgTXCQ3Fb0UAgkNgIGBF1JPbhRP8CgZSFwOzZ60D/cRCZINEwgBMMokSHDhFS9omaN7ft/DBEo0kTggGZYMTTGUbZiTJ1qo5Gy7Ce8Xte7LHeQM+eBDoigiGKqRhuUatWw2nduv2IhhQGKWPM9J+nT18MSotw/UdVr0xp9sUXUTR/Pr4wJy9smGjZsi/16vUI169154fhqfKkbdKkSBg//4VBYyEiWC7AqIFUJg0KOxavXUP6lAOVmJhX1KnTcDh75Ds/9MPnaGF2jt64Qcj8IhozJjUiZPzxu88OzHvgt+w4GgnDh7eDUw8XuB3FzY1p0grCQZzFjl6sN92x4xiMZV1x7Z4B3VIoNWv21vnBLRnzLDj8l1/Gw7kUClqb49Aw6oR79TnrHYu9AgGBgFMhULZscTp2zFuTMR896kJly5bQpC/RiQ4BnmO0bTsUBvKdoAsz7/wwxModYXodO+r0tDiLctw4QlvDGm/fc/b06tUuoL3ywzOnFTJz+yKDRyxz3yIk3gkEkhaBS5cuaXZADrA4fvyYZv2JjgQCWiDg6uou0Zpr0VdICJG/f3otupLVR48ePWEzGo51Xhqsk7BQ0qgw9VUdiNx+ylzsoggEHBABTC1FEQgIBNQgcPv2YxhAFyNCOlKiWeE+Ro8mOB6IihZV0yPBcEcw0On0QZYtI/rpJ5148vDhBHqFhH2y42PMGF22CBzt0hg+/JCIhbRcXWMhHnkAEeUHEHntiodQTVBuBQlaBgMIIyOjIUh/U3IaGWxW9bZFi1dIJd0GmqMWqto7SqMpU1aAnu0B1aun3GnGc6fRoyPR9i9cqyU0wZVx0dkvEMLvQIUdn1WrPgOlnLpBMVbs3Kxb1wNOkG9hECqM3y248RysMGVe27a1kY22EfyxILlXUdKm9aM//vhRRUt5TXbvPolo39EwfIWZaDGZ64Gxb9r0NQybL3CvHgzn9Ui8t0OoxtxBxDaBgEAgWRCoXr0kNKnciDU57A08XLHCF/e+GslyHkoP+upVLJy/BzB/3E2nTl3F/ZozMF0oQwZ/3N+KwLleDzStBZV2q3n98eOXwMATjCCAaEVRs5xlyUFBtWu7Q0srNc7FDUEW4dDUi0P0qrukSXbmTDz2V4ImW6s3ouean4DoUCDghAhcvXoVjuGjdPHiMdwbI0AnlxHz9HL4HVVAJmziGVxfvYJXUsMSFaVNZrmGQxJdveMI5M+fF7+rh6rtPobwBQf7Qby8guGmRH/funUbBIRVx7ypN2gnDxNrh9jzO+PMD3Z+TJ8+M9HHLg4gEFCLgHCAqEVOtHvnEZg+fRmitsOwmNNBgexc6CHo+P3tAWfIEIJgLCEqnGjCBKK9e4kGDyYsaInYwVG8ONO5EJwvOgHOCnhWcvaHsYNEP4ZHj+IQCbcdi8V/kKUwAg9XlVZbfYcp5PX06eswYLsoWoRbOnWm2nn27CWiQF47rcgvO4R+/30NUtbVLzA8PTm7IYxGjLgK7n9LaCnbzkKmjhTFyVFoc+euBVWdOoeA/uw5I6FWLTfcP7wd0vmhH+eoUR1gULsGupLzip0g7PxYuXIQjHCB+u40fX3+PFSivVq5Up7zw/DguXIRvsdQ6PgMgybL70KjwhAc8V4g4KQIeHt7gsLxQ1q8eBv0fmJVnwVn8np7Z4aOVTbVfSRFQ34e8bmOGTMXc7tXoPOMkDIM9fPS+/dD6OjRW3gm78ccJQB0F31AD4YomWQorM+0YsU6OGmiVM27Mmfm7OjXcKjnwH2/MzJNb4DuNQr8677QCckLA1ROzBXckuHMxCEFAo6HAN8bNmz4C+u/kXAGPwUtbQT0cKIkfUqOND940Bf3jVSUL19J+v77YVhDYnGpcWFj6H1esGpU0qVLp1FPohuBgDYIVK5cl3bsOIrnj33OPvxcYe9xpx9+KK/NwBT0ki1bdlqyZCk9efIEVMG7MI69YCm4IWWF5ITAye3bt+nEiRNYA4bDzmF+XsVZH6z5wbRXIvNDAfiiarIgIBwgyQK7OKizI8CG7vXr90Jn421k+po1umwNc8LGSs4XulSSEOSZMzpnBzs9+A/PH3jndVQALKLM1Evs+GjY0HrvLCrJDpSWLWPAf98X6Y69oO1Q3Xqjd2DvjRsPKVcu+4zYhjBlyuRKrJ+RJYtzTtBXrfqH6tePwQTG8KyUv//oI4Lo4Qtcq56ItFfvTNEf+eJFokKFsus/ynrlaNhDh87D8HOebt68ARq4eHwvmbH4KwLu9qL47YCzS2U5d+4mZc8eA6O+yg4MmjVvHgHx6M0w3lQx2OpYb93d3bCIHgFNmEm45x2ikBCkntkoHh6pKF06f8n5kZhRxwMGTEeGWyjxPVNNKYiA6BYtXtDIkXPxB/5BUQQCAgGnR6B//w6YMx2EPtVz3KuVnw4Hm/Tu7YtMg17KGydhC87yaNt2CIybl5AdEUFp05oenJ0GPEds2DCUzpwJBc1qP2QaN4aRpZVk3DBtkXhbJk9eBHrNMMkAq/YotWvH06+/3pWymdu3ryd14+7uKp0LG3yFBpFaZEW7lITAs2fPkJHeDhqUpxCJ/RK6Z6Zn9/nnYdBp4aC93dAC+BTZU63pxx8HE+tbalVKlSqN6HhM4jUoqVKlxvz9Aw16El0IBLRDoEWL1tSgwUSsRWIQAKm+3/37mcGjlJSRlVzPMc4Ga9bsc+nP+EwePHiAjNhZtHHjBslRwoGJfP9wdXVBxmUe6tChA9ayjTEf8TBuKj4LBBwOAVeHG5EYkEDACRC4cOE2sfGMI9715cgRnaNC/9meVxZ85P4MCy/kOar++HHmiCRE8tl2fhi25/Fu3cqC0uPRtzYTUsP+ne09a6Pg+a1Z4b54Ae6sZdeufUhbtd9hwRRDtWoRImIYD/vR2LkzFQRQkeYko3A06IgRc+HoaIVslmFYyP1BNWvuxHntgnFoCTRxRlOlSm2wMBxHDx8+l9GjaZXjxy+j/0jTHSq2MIUWi6k7ekmVyp3mzPmRli8fQIUL5wCWfmazcnx9PaV97drVQXTunESlXOHsj717j0F/57Vd8HXo8IrWrt2NtO9ou/oRjQUCAgHHQCAgwAfzo95wDvjS48fKxsQMK+3be8Ph2wpzPBXeE2WHU107OjoG2R69YRA8C4eAeeeHceecJbxpUxiy+VaDrnKe8e5E/RwVFUN79hzHs9j+w7RrF4rsvdX2dyR6EAikQAQe46bXtGl1sAgcwLzNvPNDf9o8X+cAuy1bQujFi7lwkLaCxo59cyp93/xav3598vPzM9yk+r0vov4+4MWxKAIBB0IgMDAQwRZN4Gg0MAgZjY81xpnNY9YsHbPH3Lk6Gw/YpqTC+lWDBwciqHW4UUvH+ZgpUyYaNGgw5g9H6NKlK7BFncT69bT0fuvWbaDZbCGcH47zdYmR2EDADl+ljZ7FboFACkaA6ZOKFk1oLD53Tr32hzFUrCGyYYPxVqL+/QlZC4SHLVHFiqb7bW3hCMF582KxwB8CJ8h8OHBS22qSYvdzpsaOHXz+9qWt6gFiqrH06eGZctISHHxd0rDRYvilSkUhOyodrrEoYoo2tYXXYcuXe4KWy7bVhCmaunQZgeyml/heX0kZUsbH/fLLcGJKrdWr/4aT5l/ob3SDEamqcTWrn2/duoNoF3SiQeFsmxie+TpJqV69FOiwZoNv/TowPoZU6WAYGEMQZegOOrk80EUpCQ2Y8klCJ7V27X4s8KPg5LIPPHZi16r1Cs7ho+jvffs6E60FAgIBh0CgevXSMCj8gN/0BFAyhMJRYHtYrNf73Xc+UvRjp06f2W6QjDWGD58LbY8bEAhX9ixKjSnP7NkReO5tACZlpHt2UpwGO/pLlSK7ImT146yKR/aUKaf1H8WrQEAg8B8C7Lz4+uvG0EW7A425ONm48H1h1KhwGjBgH/388yCsNUfKbmutYp06dSWjaGhoqLVqsvZly5YVmSx5ZNUVlQQCSYlAnz7DsfbZBpq52wj8entkDlgdiZ8S2KOkwlTpHBjI65aAAN17tuekSuUJu05rrKNKOEUgJWeJaeXYfIuWeCcQSDoEhAMk6bAWR0pBCISFsYhcwoUnRw6CAlGTwv0wDYNhYQosdrLwRBWZhqpL4cJs8AuhBQu2UKdOn6rux9kbliyZF8ZchD9pUDi6w8PDG3/sUHHOEh39SrPrlzUVCxfOhWiRaDjywlQbPWbOTI2I0Q+krAJrqLIxvnfv0eAGDwOfsbWaPNFk2qN4ZIa8xO9oCgz4z6hzZ/nGLq2zfDgCz9lK8eK5oUWUG7zRzZJt6EeOHEfaecJ7sNrBlC8fCWfdKeEAUQugaCcQcEAEGjSoDA2PSbi/Dwdl4RMEfoQhA1BnfNAPl40RPK+aN88bkY3eNG1aPxgxEIHiwOXMmRvQm9uBbA51znN+Bk6ZEk5t2vwCqsr5eCYm/lJQFzQUrgmqbDgKDQ2XDEUuzvgA1QQF0YlAwBSBX3+diAzlm4qcH/pe+Kc0ZEg4DLlLMLdqTiVLltTvUv2aCjebHj2+h9bIaFDjhqnuJwA/+lGjRqtuLxoKBBITAS8vL0kb8quv6iHg4pHkBOnXj8A6QKANNj0yB/eBpU4qK1bwujSWfvopnVM4P0zPRmwRCDgfAhoSwDjfyYsRCwTUIuDl5QHxxYSLRo7m1iDIRRoSG9RZ48OwLFlCEp91x47MuWi4R/n7rl3jEQW4UnnDFNSCaTL8/dMifdP+k1q71g2G+ir2d5TMPYAVTJPCSQ2ZM6dDunpVRJN5qOqTI2ZWrAjAgqyz1fbXrt1H2vAYWrbMtvPDsCN20ixdGg4B2UW0cycOJrNkzpyJ7txJ+NuX2dSkWjRYl1xdtenLpPMUvuHy5ZsSDaEWp8n0gJcuOT4VmRbnKvoQCLxLCOTPnxXOgpng5x4EvaVyyHoIwLPaH3QNARBL94dDxB8RmoXAf98TDpB5Du/84O9u2rTF0PAIk5z5ar/L3LmJKleOhK7TQbVdKGrHDgt/f40mGDiyh4cLsjljFY1BVBYIpGQEIsGns2DBTGRvqHc0snN05MgQGj++v2ZQde7chfLmzYuod3Xpumxcrl+/Ae7NMtL4NBu16EggoAwBzk5atmw7fj8FkcnhivfmnR/meuVn2bhx45DR2VE4QcwBJLYJBDRGQFheNAZUdPduIFC0aE4sHFlI+W0EMtNWsXB5FQ3s4NxPkSIJsTx8mCA8RYjMSbhdzacsWZiKIFwS7c6YMdBsFyywuWnTv4iMPgkRu2vSYjNtWn/QGJSgGjXKIcoon9l2zrSxc+eW4M+eShMmGKXbKDiJWKzB//jDG7RKTRS0cryquXJlpOvXQ7BQsX9s58+7wThdgNq1q4tsi9uI/rqADI0Y2Y67Q4cITo1AiGiPskqnxNkYTHs1ZkwoRM6Vj5udljNnhtOXX46l/fvnyBJHL1u2ICa4aEjwUtpZzp4lKlYsl529vJvNX716LWXDaXH2rNknjGlaICn6EAg4HgKcJfDRRyWkPx7ds2eh9PJluCSkzfMfZ8oi4PvUnj0nEVhgP85t2kTg2bkadFgQAUjk4uvrTU+fcuSONk6Q6Oj4JMlcSWRYRPcCAc0QWL9+LbI3ojFntq9Lpq29e/c83b9/H4FMme3rDK1ZLHnVqtWg+KlJt0FloERjhJ0fpUuXoYkTJykaB68NnOm+rujkRGWHRSA7xFq9vDIj6+MKsW1ASeEMqc2bN9OwYcOQDTJQSVNRVyAgEFCIgJ1x5AqPJqq/0wiULVs2Vf78+YcVKFAgFn/x+BtiCxDUyYq/Mfg7ib+X+IvB3wP8bcJfa7RPlmu4aNFc4MGPT/CAY2qFXbtsnZG8/dyPscbHnTuEiaNOAF1eL9ZrlSjxCiJWl00qhYVFUr9+05DC2ZoOHJgAnulN0Eq4gOiEK/TNN8cReTefBg7sh+j+DrRt29E37WNjX9O+fWdo0qSV4NEeg4jLUWj3G/3110FiwWJHLE2bfoTI7wzEBne1Zdq01KAUq4rsnAxqu3CIdhUqlAQO2vAxMZVI+fKFJQPF8uWjkC1VlRo18pWoRqydLPOjDhniib9scChNsClCu3fvaVDRPYRhy1qv1vflzEn06adhtHDhFusV/9tbvHguOATdIBgpq7rVSmvWeCEKuZbVOmKneQTYGatU4Nh8TwRHMFG6dOBVEUUgIBBI8QikTetHuXJlovfeS+N0RrJz525Bf87Fbu0j/pI5yOby5btJ8n2zo//sWR9NjqXLkPZxuu9Ok5MXnQgELCCwf/9GZLepz/7Qd8tUWDVqRGM9oF12WFoIUP799y6sGz9EJpg8rUSmvWJh5dWr12AtgdQUK4WF3ydNGi05cytWzItMvzzIcMuDtUERrEW/wNp8F8VpleJuZRxi17uNwKpVq6D3cRy2IRhrVBR2gsyFQvqZM0LjSgV8oolAQDYCIgNENlSioj0IwFlRCCJoixCRUVZuPwULFvwUURyLUN/vvzZP8foYfzBZUj3+Q50OHh4eDYKDg+2f9aFDuSV16lRSFsTmzbuoYUNdq0aNeNJI1KuXTqdDbl/G9a5eJRhYXRBBEJ9gF8/dWMRcq5IlSywMiAmtuEePXoSTYxgi4l/S3r2vTCKsEdxAZcrEI7I/lK5dC6UffxyNyJ4yMCZkBZ3QFkTqxFK5cuGYhMdLug8PHhDt3r0JDhN3GMSLQ2Cvk2R40Ooc7O2HU7Jnzx4I0dPvMf5QYmO4krJjhwtt3Jgek+tOSpo5ZN0vvqgHjvQt1KqVfc4qBI0hcswL/MF5pPNkfvGJE3/A9XQa18FMRNo/hsMoEinCsTA6E7F2zvnzLlhs+dDJk6kwhka4Zj7H9WM7XX727OXQ8VDPK6z/Itq2jcF5r6WuXT/Tb7L4ytdMq1b1aN68ldS9+9sMMIsNLOx4/pxo+/bUiMD90EINsdkaAqVLF4OGzzlNaLCCg11x7ypp7XBin0BAICAQSHYELly4hXseuBM1KEylGogE4JCQMLz6atCj5S5Yc+348XgpiAePULvKvn0cIISUa1EEAgKBNwicPn0a2clvPtr1pmTJCDp27AB99pl2me2BuNmwM2P9+vXQ8xgBRoMnCI4Kp2jmgv2veCN9BWt6aDcVQADdcKwby+t3mX2NwgLi558H0o4df9JXX4VD2yiGsmUjOEd11cEKhvvOfQQ4HaLhw/1R93ebfZo9kNgoELCBADvYBg8eiOxS+9gBQiAa0rdvH+hnbrJxRLFbICAQUIuAcICoRU60k4uAC7I+glCZE/a98LcZf+y8sFoKFSpUHA+TlajEqtIn4DjpdPHiRSndAE4PPzhGhmB7T7xWxeRpHN53xV+Slm+//RITrqNI6w0lT0+dAHrjxkSTJ+ucIGoGw4KcAwb4wFD8NYTjFkLQ+QWMskSckszllXp7q64Dg/+xsa6IInybQHPw4DkcazDonEIpf36Dihbegu4Skfrh1LbtXmra1IW2bo1HZI9p5aZNI1GPaMuWA8gCOI3skDYwWv/nNTKtnuRbChTIBhqsIXD6DIV42UtEDtkeAn9P8+e7g283PUTOxuL7d17xc/3Z5suXhdKnz4mFxFk4KBI63/R15LyOH++F66iFSXTmhx8WxzUyQ4o43bPnFK6HU8gMeoHFTmoIphfCNVEU0WHFFPEEnzhxhaZPlzMq63UyZeI0/UiJGoWjg22VoKDmuE62wvn5lPh3oKb06+dNffp8LZ2/mvbvepsaNSrgXrsZEYL2OewYx82bfdCXbN/8uw69OH+BgEAgmRCIiYnFfEMbGik+BU9PFxggNZxYWsCFdfPef780nP57IdBsoZLMzfPm+cGA2lRmbVFNIPBuIMDOAD/b01dZYHBw0osXHG+ofWmIqEH+u3LlCpwsR0Gz/C8cIREI8EsLx2ZFBNGVB6WtbU7bO3duU5s2DZHB/RBBU1FS0J3xaMGgJa3p3n8/hC5cCEHQUnPQhHXCaz/jquKzQMAuBHbv3oWAPkT0aVAuXLgAGrq7lDVrVg16E10IBAQCxggIB4gxIuKzpgjA+dEAzosp6DQarz3gxJiCyA6bqzdwhA5CfbYqP0fEdYPz588jrlxX0AdbvH5AP0jgp7pwgrTNli1bzzt37iDWI/ELjgce08eIWr9FOXLkp0GDgrpzjwIAAEAASURBVGE415E99uyJAWFxx5Q8xhRWckY2d647JoHFkIXxCYzCORFNMAhG9kgIXgJABMkwVQsb3/XRLXL6tFTn0qXUmDjqJplPnrygTp2GSlkQuXJZapFw+7//Epw0BGFRAiWDdYM5RxrWr08wcIciHfkPevDgETguOyTsMBk/ValSlP78cxIwGI4MlYfQlYgw0WDh4XEWzu7d7OTyhZOoDJwF3eH08UnGkWt76EmTemNh0g20Z6GUJo3yvpm67erVLDRjhmUfJ4vS8h+uCOUHMGjx+HEIMkjiNaEC4W5LlIij4OBrVK2a7UwAHx9POF76QbBuCHRKwqRMFoOh2Xw7c2YqiokpCAdqHZt1RQXzCPBvtkcPLwjSh0oRf+Zr2d6KdQZ+12nxe89pu7KoIRAQCAgEkhGBwEAfUGwwHYw2TouQkDjMYbyT5Iy6d/+K2rU7QdWrh8Hxr+6QO3fy/DcLtOg0ECtTNwTRSiDgkAhgzSzRMrtrYNlhO27q1PAeJGLJly8fgvzyQSewpeKjPMJi+Isv6tLo0ffoQ5lJ1IUKEbJPQrC+nikFKgoniGLYRQMrCKxf/xechglZNaxUt7orIiIClOJ7Vf02rHYsdgoEBAISAm/DvwUgAoFEQAATMp6KncNrBTgukBtB1i3l/40B9QvgLefFrjd0fvy3W3qBI2L5f5+hk+ZV0HBfYrxn8ck5czaAV/Rr+vrrIDycxiGy5DgWo7FI1dU5JnhRB/pGyTFw4ID8UbBTY948d1qzJisMq32lhu+/XwzUBD5S36xR0aKFTv/j2jX5/Vqqycc7dIgzM2aAwukwBKfHwckSBuO/pRYJtz98SMjkIFqyhJ0fCfdZ+8QZInPnRtDBgxtpxQqsZB2ocAbEjh3T4RTqCcdTXkyq/eAI8YcgmRc+eyPSKIAqVfLFd1QRtEUjkTXSP0U5P/iryJEjI66Jb7Gw8EF2hrIvhx1iAwakwW9ksCR6qKy18tohIeFw0vyX5668uUmLNGleS1QgJjssbODfZ//+3cE57AvHiYVKRpt5UTl4sCf9808+ZBANMcmSMaouPlpBgBf7TKnXu7eP5BS2UtXiLnZo9u7ti++km8U6YodAQCAgEHAUBJhKKjhYAwsnTojpYeLjU0ti8ElxfuxkbtSoPo0Y4anqcBwANGCAHwIs+qtqLxoJBFIyAnnz5oamoTZneP68O2ioyqrujAMFb926hezaTQgSWgE6nw1SxocWOhzc9/fft8Pc7YFs54f+RFKnJvrtt1DatGkWMk+O6DeLV4GA3QgcPSqRlNjdD3fAtHD7mOtRFIGAQCBRENBmFp0oQxOdphAEjsCTXU5pdsalS5ekMGwWTreEg6ur6yueCP1XVMaT6Ztbfz1z5gZ17jwcWR1PYbiMQlri2/pff00QDScYQgkibES5cxMtW0bIJiAYzJGqguwNNvyzse34cfB5neAoeR2dFWt6cF+bN3tDzLkwJokDyNdXF3XDBr7evb8GHdYMTNjCETlH4E8lWryYkHXy9vhq3rFDhTNUevS4BH2GMUgNjobjBQOUWVjn5KefSLbDxLBb1rKbOjUCjoZfEQlYhjJkAAm1gxRcU9S48QfSX2RkNITuryNb5TkM+i7QpshE/2fvOsCjqLroTSGBFHoV6b2J9F4UpQjSRFRApCmKKCJVQKogUgWRJl06iFSpIl16771KJz0kIST/ubt/IGXLzOxsspu97/sguzOvzZm3M++9e+85xYvnUUXR5CCXpaobLVrUNogF1q8/kcaOjUREhOXi0Qh+mjKFQJ1GoNBKl2x0YN7eabCB8+L3b7mTCs5GRLjDK9Xs48ZkDc2b18LvPTcMZSMQNROIiJCn8GhLmpUNH6tXu9G0ab6gbGoKHuK2ijROktYkR+Ij0KxZTRgkt2FD7Cgiy6Lin1L0efRob3gSV0fE3muK8ksmQUAQEARSEoE8ebJBu83T4KCgJUozft937CA49JSJf8jun/v1+wR0o5fguX0WFJCRmFspa/K//wgRk34o9y202nIoKyS5BAEXQqB8+TpwLjuEaNbnNl/1/v3p4RhnWX/DVCMsRj537q+Yly0GjVUMlS4difXvM1BceYItIC20I2PBBtAclLc9KE+evKaqsHpsw4Z15ONzGmtI5WvW+JWyEWTSpCDq1u1TCLMfE0ek+ODIZ80IBKj1GrTS0l0W1JQkCAgCdkFADCB2gVUqjUMAhow7cZ+1/D1y5IjZOH94klRkIwHS87Rp0+rk95K0lyze3L37cGzah0AAPOl53tAfBxUSDs3v1Mlo0GBh9IkTWfeCoKdgFC9n77UyWGtWq0YQCTcKpfMxNkbcvBkDCh4jtVaJEi8nha1bvwnvmc2ItDiLRSMLjBuNIKwLki1b0r4oOcI2ozFj2POZIFRNENR8SvXqKafVOnuWIPJFL8TflbSZOA/3vUuXMERR/AEDj+NQYcXvJ3NWV65cPP4hl/kcGBgCWid3CAoax3H79sZxmyuXcZw8x/qKPc22bjUa+95+2ziOd+26S40b94DWxxRQuelERmwG9VdfzQr6I/0MIOfOecAg+PK3Z6bZJIdZ7H3Xrt9AobYTxs7l0BF5gqgoNxiDnsOLxw04ucOI5obfS038lltBMwQgStINAY7EatmyD4WHX8OCPUoRJVoUbCU//OCN8VMSz9YeuvVFKhIEBAFBwJ4I8Jy3XbvG2ExcQl99ZaRe1drerFn+mAu21lpcUzlPTw848Yyg3r0nQmB5P02YEEaFLLBZ8VxjxQoPA+3oxIl9MZ/W7pWuqcNSSDUCYWG8ca7QsqW6dilgDoFWrdrCUW4m1qEB2NQ3l8v68Xv3eE3qg/UhFoiJEjsd7tq1C4aDVXTixCEYY58YHMTy5s0DB6LMdObMfjgEhWFtEJ1IjyQSNYVBI4FgHJmHKPM/6eOPvwEdVXfVEeO//fYTnLNCEvVM3dcSJcigsbl79244wNRWV9iJcnMkwX1QNvB9y549O6L9jA6WTnQJTtNVDw99t1TT8OaSJEFAELALAvr+Wu3SRalUEEiKAITQX8ELHbEXhrT41KlTAUlz2X7kypX/4Fk8nJYtC4G3iuX63nyTENFACKslYi2EJUsIEQRk8PSuUyeG+vePNakV0KIF0xBEgBbnKBa232Ji2AIbqW0MXim82J0zZwg2lb+BAPpdeL9FGzzyWWtkwQLlRov4PZ8/n+CZY9zQ5uMHDhAmovFzWP7M5TvrYLNo3fo5BOS3IpKko+oJsOUeyllbEOBop2nT5iMa6SmikQhaN4Txz6LvhIn0yzFXpAhR9eqEfEYDH7fZoEEsPFMfYWPmJ2xy/GBLN6yW5Wid3LmzwKMsRLMQeVwjvCi7fZs0e5amSeNJH35Yz/CPI4fOnLlOjx+HUJo0HjAw5sHvLYt4mcWBrfPftGm9sKAeC0PqdGrSZBf+hhqMzKY2ADgKb+dONn744ZnaEJt/HSUSR+f7IdUJAoKAfRH49NNm2LRbB8NvYIJoZDWtrl3rjg3KAtjkLKimmC55+X05aVIfbKJy5N6voLIMhld4KKLxeJPOGB196RLPpb1ozRoWTy8P7bUvMX9GKLUkh0XAwyMUzlF10T93rGnc8M9hu5oqO/bqq3kof/5ymJPvgMMNJjsa09ixfnBQ65lkzrpp0wYaNaov9CnDESUfSOyIlzMnPBBhpOzd+55hfbB588v1gKnm06YlrHFj0L8ArP3GwrnwAJwLFyiOrP8PoWBRUfcwrzZVu7pj7doFwnHpt1RnALkHC9Zvv83Eta2k0NDQF+trph/z8fEB9k1x776Adp6VTQ11cLp87oIFC2AdeUs3HEwZIHWrXCoSBFwcATcXv365/BRAAOLlcdPiYYgQGaq2CxA8T4eX+DaUw/YrBeGlXvry5cvYvtQ/1alTMPbbb6+q5hnlnrC4btu2BGopIxWWkt4xJ3OfPumwIKwJ7/veLyagwcFh8Kr5EYuLM9SqVRh4kAnecwR9ipcb0krq58npqFFEa9cSKLeMJZgKi6NQTG0YmqqzTh0CfyqRr6+ps+qOffBBetAnTfq/KLa6spI7DgE3w70zLjbjflpx59T9ZS+h2rU/gw7IDVA6qSsbl5v78cknvqCM649NZgwuO6Z58zbR8eNT0V9YMGxIy5a5gZ+4MbzKvrahFmctqt/4SWkEDh26gHs4hy5cuAyKvxhQQYQZjHghIW50+rQvjL1uVK5cSTxjOyLiLvk3/lIaH/u1n3rGkP0wkprNIyDjxzw2ps+sX/8vnnVj4JwQBtpJ03nMHWUduXbtMiBqeQY2MDOby5Zsx48cuQj9tYN05MhJeJQHGozSRYrkRcRzOcxza2A+bC2aVMZPst0siw0FQgT4LeRwe7HpajG7A538P5uAoUc8D3bWxJvfTZtWA+XqE8qRQ/1VMJPB5MmvwfC488U9jELI7DffdMJvcxcitoIMDnTxa+Y17okTBMcpUhSBG1eWYR48mBeSrbAunRx32OLfDRs20L59XbH2tS0ChBuBbQBOM6/S7t1nLLap5KQjjJ9nz57BSfJHOE3OMRg++Lup5OHhAeO3P+h4W8MZ6AdE7nibyibHVCIwYcIEA57P2SJoY8qADZp58+bRm+xZm0oTrlH2oFPpvXWGy5LB5wx3KZX10RYDSPHixbPA4LEGkNTAvyhMOhpBXB1TNvukDz7wjZ02LUx15SGYmzVsSKB4MtJeqamAvZS7dfOhmjXbwZP+/QRF167dC/2A2aDWeQxPg3Bs4rGeBhm85hJkTPSF50FMe8WTWxYujz8xZp0SNoAoSUwdw1Eue/cqyW09z8iRabFR2Zvef7+O9cySw+4IHDhwjkaMGEi//27b4uIM1hPDhhVGGDxWRHZMERFRoKVrh02gAHgzaWuIjY4NGvjTunVT4T2XU1slUsqhEHjyJJgOH76ICLwr4J4ONSz2ypYtAqNeURh+dbDcOtTVSmcEAUHAFRH4+eelcGZZatCIYz05JenkSYL3b3rw9P+AuVcJJUUkjyCgEIEACgw0GkB4k1VSyiCwdetmzOM/hRZloNW1Yfwe8jqwV6+ciBzYjvm0cUIdDZG/jz9uiuisw6AYjUjiKMeOfqx3uWULqTbEctu83m3aNAOc+pZgzVszfndMfv7553GUMeNwGHBNnlZ9sGrVTJgr3lBdztEKBAYGIoruHUTEXwUdbLii7oE63HCfN8MzMptWTm1FLblGpjt37oAVoTpYEGwnJMmaNSscuS6ARSD10mClT59e9qBd46fhkFfp6ZC9kk4JAiYQKFSoUGEYPzbgVFH8ewrjx4f2NH5wFz77TL3xg8sNG2akiWLND7WJRSHHjQuHAWUJJjQ1EmgGvPtudYSvVjdQ7GzffhzRJUtBD/DEEA3SoQNBpBvB5ygfl5jLdeVKwoY2TzKNkRuJnT3YC4f/KYkACQMccBzRLWXMGIXJQijaRwckaURAP+/HefNWY2Fhm/GDL6JUKYRmBd2nO3ceGeifNF6Y1WIsWj527LfUo8dogyesp4Y32vDhaal9+5YG+ivXHIf6jR+rNyyZMrDH8NtvVzD8S9yka97jxCjo/T31jSG9EZL6LCEg48cSOubO9ejxATY4s8CLeTr17BkCWppYbJiYzs17MtOne4N2KhPoWYcaIuBSz7NQxo/pu55yR51tbDmCB79ed+utt+qDlmoGngdfIMIiEGtJWBksJHaQmzw5HaizsmMevQ60erlfrMkmTBiJufFRGEYiTNYwciQh6kCb8YMr5PXqxIlBoML7ykCJZ7KReAejoyPNPuPiZVP8kdvXY6ym5PjhCJ1GjRoZNszZYKU0RYD7lw0mb0PEcc+ePWB1EOcgpdiZyvcKuMWZtoo1cphuTGtiw1R7CG96YkGrx9jU2g8pJwikZgQ0bBelZjjk2hwVgcKFC9cB5/8f6F8W/HuIl0sz0F79a+/+liunvgVQlMKjxBhxob60sQRrL/TpE4LN3XmI8OiXpJpixfKCAzUvPPmawoAQAoPLAugP/AVu1OcGaip2vmJdA3bqaNKE4N1uPkqEOVy5z5jzWk1eXsZ6rWZUmCEiwgOTWU+KjtY+WVDYVKrN5unJFi92pIi1GcdDh85Sv6TDTRN2VapEg3LoPAx21TSVV1qofv1KCGF/G1FTm2EQfKp4ccQ2t/Hjvenu3WL088+tbcZOaX8dLZ+e48fRrk36kzwIyBhKHpxTaysyfrTf2ffffxOe02Vp9Og5cJw5iM8x8NYOp1y5Yg3e1Zcve4BaygeUrGmoQ4em2Jx5j7y80qSq952MH+3jR8+ScQ4oPLdytjk9jyHexOYNR2fru6l7WK9efVCA7oH2ZFfMb0/DsSkI3ukxVKCA0dmNo/lZ32/rVm84yfmArvYD2rhxqIEOKe76scZGhNls2rzZdEQB6+bx2pG1AG1JrCeYOfMTaP4cAUWp5UV3pkw56cEDtvLCamNj4j3q6Gh3Xe53So6foUOHGQwZaowfcdDxRv3t23eg4dIbew2/xh2WvxoRYAxr165pUxQIR+P06tVHl3Gp8TKkmCCQ6hEQA0iqv8XOf4FFihRpg4npPFwJz3pOwhDSDNoh1/HZIdOiRayBkDASQ0tHGzdmz5rDCGeNgHBZWrNVsLfzzz9/CeH1PRCKDMBL0yhKB62zBNEg5iqoXJmwKGZhOnM5Xh5nBxE2rHAbcYudl2fVfzp3Lh02yPOpLygl7IJASEjYC20YWxsoWvQpNl2u2d0Awv0cPrwrqOHSYhG3Hr+FUCz8LPf+7l0O9feF9+zroITrr1iA0XKtclYQEAQEAUFAEEheBHLlymIQFQ8LiwCd6Vk6evQcnALuGd5rBQsWxOZWMdD/FcF8kJ0lJAkCgoArIJArVy6aP38tXb9+nZYvnwtjyG66ceMWDD2xeDakAWNAUapWrREMHB9R+vT/F4WMB8zMmeMQWRYIo0i8g/E+7thhdLBTwh4Qr5jJj82aBSACZL1VA8hrr70ORyemIXhish41B69dIxiE8qop4nB5b926SYsWLaSnzOWrMUVFRULX8y84VJ7B2gnh+5I0I8BRIOPHT8Dv5huwIASpriczuCyXLFkCOjnzez6qK5UCgoAgkAQBMYAkgUQOOBICMH60hPFjAfqEmAZaDz7Ej86cOQPpMsdN+/YZdTls7SEbGCpWJIg8X4GHjfVJSUxMtMH73RwFgrn+tGlDEFhXZgDhOnhz+dgxokqVzNWo7DjrhJ06FQO6JDGAKEPM/rn0WMjE9ZLnb48fR8Z9tetf9tzr378DvfVWNYg1jqEsWYLpgw9CoA9ijGzi63r82DhuV6/2xfj1htGkGzVrVtOu/ZLKBQFBQBAQBASB5EDA1zct1atX3vAvOdqTNgQBQcDxEcifPz/17TtMVUc5mmDHji0QJzdfjIXPITuhS3r9dSzw1++1WldJLEBPnnSDYyDBMdBqdosZNmzwpjp13rWYx9FP/goF+hAWHbUxsYbIzz9PpJkzZ9lYkxRv1qw5sQB9//79FEeCsGYSGz+WLVtGZcCdHheFJWgKAoKAfRAQA4h9cJVadUAAtFf1sbG5BFV5ICx5zqVLlz7FZ4fnSoIOliI6KSUQlSkTBgPIZUUGECX1mcrDIdGsd7d6NYEz1lSOhMc4UmT+fNsNIH//DSX7Gq/Du8grYQPyLcUQiIlhCgBlejDWOvnokTtlyqRQmdVaZQrPV6xYDFy2s0FBdwFh/Ztp3ryzoLgKNOjbZMjgA4NIcVDFvUFz5lQQb1iFmEo2QUAQEAQEAUFAEBAEBAHXQODSpYuIEHG3SCn74AGBak8fPJiC+QFXaCV5gYe5ceOWtGLFHDAtwItOY2IKsCVL0tGGDR001uAYxdauXQO9F+04xL+K7dv/MVDAsUOZJNsQaNXqfUQzladOnTpCC/O2WUMIR2VmyJABzq4VYXyaCee9LKL7YRv0UloQUISAGEAUwSSZkhuBUqVK5YQFHWRSxLvjy53F+ME4McuAXvOHTJli6P79QK7WanJ394QGCADTYE8YPZpAVWSMOGFjiKVUrx4RC9+dPWuMBrGU19w5Ft0bO9YPL/y25rLI8RRAIH/+HAiXDzbwBNva/KlTvogsKmJrNZrKsyGE/0kSBAQBQUAQEAQEAUHAFAJBQWEGmlk/v3Tk72+jS7mpBuSYIOCECJw/fx7R/gizsJB4rWuD1nOCmnkP38NDGUVft259sV5dheiTxwadywQVKfwycSLTL7eFk1YmhSUcLxtTLHGkgV7J3d0NeiC3KE8e56YF0wsPW+spVKgQ7dy5C5SUR2n69Gl08ODB/0frxGKPyM1g7KhTpy7WyV8QU1UataxsbVXKCwKCgBIExACiBCXJk+wIREVFjcQLIiv+3fT29u6EDqRI5AfrXailYmQPer0S03p6eyvjgixZMh+MEgEQwFTfOoulT5hA8I4n+v135kU1XwdPesePJ+rRgyCQR5Qunfm85s6wAaVevbcQ6mmhIXOF5bjdEKhbtypt2nQNXLDRNrXBhrgjRwhjsZBN9UhhQUAQEAQEAUFAEBAE9ECARX///vsYokBXgIL1GoweseTr64aNqVgYQtzhtVuYunRpTbVqlTFsUunRptQhCDgbAhERkVjbWV4H5AN78ZUr2h3h4mMCvXXKl0/ZejBr1qz03Xdj6LPPetLSpcFmNUri1x//8/btbrRlSy7oXgyOf9jpPt+8eVPXZxRHkly/fkMMIDqPhPLgYp4587cXtfI7SPS4XsAhHwSBFEFADCApArvrNFq0aNG/cLWvmLniz3E+AekSDB7v4F8kXhDtuQyor3wjIiL2Ip+ZKoyHkW86okSmW8yk4STTQrFRQE3y9ycCnSZlzKimlOm858/7INy3oOmTiY42a/Y2aH8uYNNZmxha9epGwwZTXHXoQFgEmhc6r1CBoLdA9P77CM9Zro6Lddo05nr1oxMn2K4lyZEQ6NChMdWvvxoLixB4Y2nv2bp1bvT221WxgDKjnqi9aikpCAgCgoAgIAgIAoKAKgROnrwKb9sfqFChYMxvQw0ae/HnOZA9gIj8YYgsn6chQ7LAa3cQFSuWR1UbklkQSA0IpE+fni5e5Pm7+QgDdrY7fNjIHmDrNR854oG1a23F1TRt2hLUQtewPp9MM2YEU/bs1ouyc+Iff3jQ5Mm5YDhZD7YEDXQJ1ptJthzY99CVLonx4Tol2RcBMX7YF1+pXRBQggB8uSUJAnZFoCRqL5voX1yDORIdLwvjB89IYEKgOONclsR5TH1HuZw4rnvizfpIlTrObBz49199unLwoAdVqKCMRqhFi1q0bZsXPXqkve0qVQgb4GygICpdmmjoUKKtW4lu3SJQcRGdOUO0aBFRe5inNm8mCJgTNWxICO203ib3q3Vrot9+c6M+fTqK9od1yJI9R7ZsGWG4qIUNgDSa2w4OZkOaH/Xs+bHmOqSgICAICAKCgCAgCAgCeiDw++8bqXPnvjRmzH80bVoo8Vw3vvGD2/DEqqNmTaLZs0Np4MAb9MEHPaGNt0uP5qUOQcCpECiNBeDp05YdmJgOedMmmEjM20gUXTPvuS9fnh6UVC0U5Y/L9MUXvahjx0nUtGkWmjvXwyCMHncu8V9eu37ySXpoftSmVat2QLtEJ/GSxA0l4/ccOXgLRT+9Dk9PDzLWmYwXIU0JAoKAIJACCMRtMqdA09KkKyBw8eLF/BqvU7+3usYOcLEmTQgLJqLvv1deCUdQsKZGo0bKy5jKCdpIeuWVPOA4VRZKwmLigwZ9Sv36TcUCzjJ3q6n2+Njp0wTOSqLHj4l69yaCExDoAggeNkRXrxqpsWrUIPrqK6NeCGudbNlCmIQSwmaNfytVMubjc0+eEETcjVRZBw4QffklG4diaeHCdcC2OiZbzsu/ag5DRzkeFhaBxckheGgdp0uXrmOREo2xlAleVmVBP1aBSpTIa7Krw4Z1Bf3DYapc+QFVrWoyi9mDzOP79dc+oEdrT3nzKnDJMluTnBAEBAFBQBAQBAQBQcA2BFau3E6zZs2iNWvCDHNaJbXx3GfNmlAYQSaBBtcbjj6wmEgSBFwEgbx580IPwp3YoYnXgaYSsx288QYhmoLo449N5VB2jNeYr75amnLnflVZgXi53n23OVWrVhNr1AlUt+5yg1Ne2bIhWOtEGQwzFy6kp0OHPHANeRH9NQAOXvDwSyVJb2MFUzMVLlw4laAjlyEICAKCgHkEsEUpSRAQBMwhgKiHWI5aaNbMSAtlLl/i45y/f3/CxCzxGWXf2SPm/ff9wHP6PdWpwwE0yhKHr7ZvPwSTmOPUqxeEGFSk27cJiz2jV1wRBJ3Mnp2wMIc6DxxI8KB5SY116ZIxGmTKFKMWyJ9/Gg0eHDHCiSfOHCb95puEiedLj7stW9wgpJ4VHKyTxQhihErz/yychggoQ+hydHQMuKzD6YcfZiGKZzc1aBBNlSo9JWaQS4OgjgcP+P6408aNviiTlUaM+ApjlIO0EqZr1+5R8+bfIgIoAPdNWUg0L5R69PBBW2/h3nZLWKF8c1gEEo8fh+2odMxhEZAx5LC3xik6JuPHKW6Tw3bS0vi5ffshnJG6Ya4ZAsFj9Zdw9y5Ry5YZaMeO3yBaa2YnWH21qbKEp2cwBQW9hbkoz0ed6xItjSHnuhL9ejtu3HDKkOEXUOKCG85MCggwOvv98QfBgGEmk4XDvG5o2DATjCg7bNaeYEFwFm8/ceIYnO8eYM3jhbVwcSpb9nVQZNnXISulxk/Hjh2gxbnGAsLKT9VE+NuaNeuUF5CcuiKQUmNI14tQUVmWLJlkD1oFXpJVXwRk8OmLp9SWyhBABENsWBhRp07soWKkhGKvF0spKIioe/eX0RTmvGcs1TFrliedO1cdXi0DLGUzeS4q6hm1bfs9Zc58gYYNe0p+fiazJTi4ezdR375ELVoQzZtHtGcPoXyCLIYvQ4ci4BZPjSFDiFggnumvWDwdGl+q04YNbmirCBamkwwb+KorkAIGBOJPmnbtOgUB85HQcAlGuHc0OG7Ng8Qh4X36+MEAUptGjfoSdBAeCTLzxkGXLsOxqLmDaKBwQ4RPggz//8K82Rs3ctSTPyJ82mGxBOufJKdBIP74YQOaJEFALQIyhtQiJvnjIyDjJz4a8lktApbGT7t2g+DAdARGEO078kuWeGBTtTZNmdJPbddcKr8YQFLX7X4E3uJ3361K69Y9RkSF+Wv75x+C0xXRsmVE0CdXnEJDidq0SU/t2o2Cw19bxeUcMaOlZ5A9+3vy5Ems25tBdzTQpmYyZMiI9fh8ql27tk31SGHtCKTUGNLeY9tKigHENvyktG0IiAHENvykdCpH4OFDiuV9YUSGgmOUaOZM9gYjatWKqGBBozGAIeDzly8TrVhhpHtiiqi0aY1RFEuWqBNEX7nSA+H6ubCpPJn8/X00IcyhrFOnroLexjLQUz2F9kZ0kjBm9tBimi2mt7p3j2jSJGMESOXKhIWe6WZ5s5vF0fPnN0aBpEtHiDQxnVfJUY4YqFmzM/rYWEl2yWMCgbhJ0z//HAf91DBMYkMNY9NE1iSHmLJq2LC09PDhayg3JIkRhCOKFi/eBk2QxfAEC0M0SQQiPKKg38LRJG508qQv7dvnBsqs8jCgdcS4yJmkDTng2AjEjR++12IAcex75ai9kzHkqHfGOfol48c57pOj9tLc+Ll79zH0AT6n7dtDDI47WvvP895atfzhGDQP8yBfrdWk+nJiAHG8W/zff/+BpngjIr93gNLqhiFSPHfuvIjMrwu9x4Zw7MtjsdN//rkCho3e0H4MNmjkmMvMTlBsBBk3ThnzAdMtf/VVBqz9BoE+C4tKJ0/mnkHJcVlt2nwEquptmL/jQaUhsSh35cpVwBqwQZwRNeCnV5GUHEN6XYOaesQAogYtyas3AmIA0RtRqS9VIQDjRWyDBi8vKSTEaOTgyd6NG2TYCGZDAovAFShA9M47RO+9R+T7/zXSqlW8wUw0caKRBuplTUk/cSjwkCHp6M6dPNh0HkUZMyoI3UhaTYIj9+49oTlz1sIwsxXRAM+Iqa28vGKx4e0OTY8YTIafI1ogHNyoRoHzrl0JOiLG60hQUbwvPMcaPtyIA+t78Ga41gQDE/DKSkeO/C4TL40g8qSJ7/Obb34GIcFgQ6SSmqp4/PbrlxbGi5YwYrQ3W/TChVvQE7lIFy5cxniPAh1EVipXrhgmzsU1G+rMNiYnkg0BV5t0JxuwLtSQjCEXutl2uFQZP3YA1YWqNDd+pk1bA8/omdhohaeHjWnkSG9Q6XxDH374ho01pb7iHh6h0Iqoiwtzd0r6K74j5sYQn3PGdP36dVDYfk03b55GhEAoVajwjPLlM14JUxQfOeJJq1f7gxqqGNaov1jUfhgzZjCdOTMPTnUhFhkFrlxhGlwyRIt07kwGDUFPz5fosaPgsWPsTJgB9WXEung+flNlX2Zw4k8pOX4CwENWs2Z1rAPhyaghZc2aDRR/O1OFMLyGy3eYIik5hlICBDGApATq0mYcAm5xH+SvICAIJEWgXDm/2E2bQg36CYnPxhk+3PArSgN9BVMJ0amgw8oOQeisEBa/BWqqUGwYx2KyiaWCO4EvlxBazxRC6eiff7ywUPvIQCHEmg56p6CgMESp3IGXyHNwIfujD6/Ao+00/fjjMGychyHqhGjBAiKOWGG6L0uJI13Yg4eNO7amTz/1x3UPperVS9lalUuW50lTy5Z9YEg6SY0bw5qhIRnpzPwgTj+Rihe37BGmoXop4sAIuNqk24FvhdN2TcaQ0946h+i4jB+HuA1O2wlz46dLl6HQtduvWYsvPiCbNhEdONCQxo79Jv5h+QwEnDnyI+4GmhtDceed6e/ChbNo+vRR0PgLMIiUm1tO8hp2716C1mRGUOb2os6dwd1sJs2dOw26kGNp8GDWBTSuX01lZcroIUPSIOrEHc526RAx5Q72AXcKC4uBMdKNSpQoAcqrr+GwVQ9rYCyCU0lK6fFzBdand99tDOfGh2CkgKVJQeJ9hixZstAff6yi0qXLKCghWeyJQEqPIXtem6m6xQBiChU5llwIxLPNJ1eT0o4g4DwINGjwNv3001+IiohM0mmeVFrSWGB+0549/RCBMQThxoVgfPgPocSbET1xAtEX91FfLLxp0kI/oygmgzVp/PhqqM+MJSVJ6+oPcOh+hQpQw46Xatd+jebPLwvDxwEYNJ7DOEKYEMXLYObjwYOWo0TMFDN5uEaNUPr331NiADGJjvWD587dhHHtqmbjB7fAdG39+oWCBu13mjZNve6M9V5KDkFAEBAEBAFBQBAQBJIHgWvX/lNMB2qtR4UKESKzb1rLJucFgRRFYNq08fDmn0ybNwdbjNbgTvIaFrrXyBsIx7sxcMgLpG+/HWSy/x07fkF16zbEerg/DCuHQJ8ViXVtOKIGiJhKlymgjx7NAKc6T2re/ENQ434HJgRfRAYFUWhoGKUDX3KmTJlM1i0HbUegEB5QO3bswn38FNTEJ3Av4V1pIaWHOGkRUELMnTsPOo+vWsgppwQBQUAQSH0IiAEk9d1TuSIdERg0qBP0Pi7QL79cRiRHlGHCqKR6psr6+GNf+vTT9gbjB5fhiIuBAzsmKO4IFv8JE3pRgwbdMVm+a4hkYTov1vawlM6eZQFtSzmUnytVKpZ+/x0VStKEwG+//QHvLVjbbEz163NEzzEKD48gHx9YRCQJAoKAICAICAKCgCBgRwTu3w+AF/Ju8NgfM0QpP38eA2qejBDkLQMNj+pwEiqiqfWYmFjommkqmqSQUQtQmWd1ksJyQBBIBgR27doJHYdfaNWqYINTk9ImfSA1OWdOCLQif6MyZSohwqOByaIFwPM8ffoyYnH0vXv3gM5qL4wntw3agfnzl6ImTarQqFE1QIvs/aJ8+vQZEAGS4cV3+WA/BLJnz05//rmadu3aBS2WMXQWC3WOsomMNDpwenl5GzRgCkLAtA8W8G+/XV+op+13O17UfOfObUQPspPpfgoJeYL9FT8qVqwSVaxYCX+LvcgnHwQBQSD5EBADSPJhLS05IQIckbF8+Wjq2nUktW9/GuHvYZTTis7z7t0cUuwPcfBOMII0cvir5siQdesmQuSxIzxCntL58wSaLsvdfvqUrHoXWa7h5VnWSwkLQ4WSNCGwb99xGOe0UV/Fb5AX+OXLE7yHroK7t2T8U/JZEBAEBAFBQBAQBAQB3RBgkfLPP59E+/efg5d4OLTFXmp1XLx420DROn36ekQlp6dff/0aHujq9AJy5MhEd+/eoqxZbe8ytKQpRw4F4dG2NyU1CAKqEXiKRdmAAZ9DrDxIlfEjriFmM5gyJZjef/9rUMYdwfrOvAZlVvygmjVrbvgXV17+OgYCTGtVp04dwz82fJw7d45uQfQlFnxnr7zyCpUqVcoQjeMYvU3dvTh48ABNmDCIAgKu4H5EYH39FJRwRLx/cubMUjjE+uG9l5m6dRsA+rLmqRsMuTpBwMEQEAOIg90Q6Y7jIZA2rRdooobBs2IXokFm0GuvReJlFQrxNjIYQzj899IlgkC0Gy1d6ocw31fhzdaHChV6xfEuxkyPsmXLiGiVD+jgwXlYjFo3gLDRgkXb2XPI1sT1+PujQkmaEAgMDKXMmTUVTVKodOkwMYAkQUUOCAKCgCAgCAgCgoBeCKxatZu+/PIXbA6FgLPevAPHkychxP9atx4Bap3qoOjsYfA4V9KP8uVfo+PHT8Gr3Xz9SurhPMePe4BCtpzS7JJPEEhWBFavXgmP/hDKn197s6z92Lx5CK1YsYg6duyqvSIp6RAIcCTO66+/bvjnEB1ykU5ERUWB6rw3IqTW0siRQVTOxGujQYMo0M09oevXn4BS7htauXIuDJALDJosLgKTXKYgkKIIpB4FqhSFURp3BQRatKgNjtPfEdXxHR061BBW+3yImsgM/Y4sNGZMKRgEPkIY8SRas2aiUxk/4u5djRqvIVTWF5NfqJNYWS/CiYROnYoradvfM2fcsUBFhZJUI/Ac1jdPHc3Y/v4xCNGFiqEkQUAQEAQEAUFAEBAEdEZg0aJtiPz4GdplwRaNH/GbZUePFSt2UsuWw6A58DJSJH6exJ/r16+G6GbznuyJ81v6vn69DzaYK1rKIucEgRRDYNGiX2G0sH3u/sknT6F1Mz3FrkMaFgScGQE2fnTo0By0byvx7jFt/Ih/fWywnD07GNFU+2B8rGugl4t/Xj4LAoKAfRDQcevMPh2UWgUBR0KA+TTffruC4Z8j9UuPvlSpUgKhsumoRIkwgxGkdWvztVatShBcI+BgPo/SMzt2+CIU9DWl2SVfPAR4PLJwvV4pLMwdoe86hPXo1SGpRxAQBAQBQUAQEARSBQJnz96A5+t0CgxUv1kbFhZJu3efghDzMtD9tLGKx+uvFwK9akbQuoZQ8eJWs5vNcPAgIco2NxUoYIX/1mwNckIQsB8CvOkaEPCA8uWzvQ2meH72LBi/mzCDiLntNUoNgoDrIPD99z0Q8XGM+vaNUHXRrVo9BzXZLVCtN4PhZJeqspJZEBAE1CMgESDqMZMSgkCqRIC5Q7t3/wj0AunAW0l05475y2wEaZNt2wiC2ebzKDlz+zaBo9kX1AJFlWSXPIkQ4HvG9GFBQYlOaPx65owvonEKaCwtxQQBQUAQEAQEAUFAEEiKAPPQf/TRSGzWhiY9qfBIaOhTmjRpFV25AlEOBenHH7+BwcUPUSMKMpvIwvrB/fv70Q8/fG3irBwSBFIegatXr1Dhwvpt57Cx8OLFiyl/YdIDQcCJENi7dw80VzZS797qjB9xl9i4cQwM9dfpt99+jTskfwUBQcBOCOj3xrRTB6VaQUAQSD4EOnR4ByGYeaB1QtQGDnZ375puG9Si1LYtQRTe9HmlR4cM8cVkoQPxRr4kbQhUrVoGXpHaysYvFRNDdOQIQdumUPzD8lkQEAQEAUFAEBAEBAGbENiz5zTdu/fEpjq4MEeP/PjjEkX1VK9eimrUqEvff5/WKrVr4gp5TtS7dzp67713of1XMPFp+S4IOAQCYWHhiNy2wlusoqd+fjGGCBAVRSSrIODyCIwbNwDRiUEEYgbN6fvvQ2jWrJ+Jo7qUpnv37kF/dgl9990X1KnTu9S5c1MaNqwPrV27BtTsEFmVJAgIAkkQsOFnmqQuOSAICAJOjgAbIubNG4YQzGzUtClRixZEGzaYvqhu3Yj27iXatcv0eWtHlyzxgFdeYSwu61jLKuctINC5c0vcM38LOZSd+ucfoooVS2EhlU5ZAcklCAgCgoAgIAgIAoKAAgTGj19pU/RHXBMcSbJ+/X7QfyoL6xg5shuilSvQ11+no1CFwSeBgURduviA+qo26EzaxzUtfwUBh0PA19cH41o/J7LQUHehv3K4uywdcmQErly5gv2M21SypG299MdSvm7dCNq0aaPVirjNTp2aI6qyOt2+3ZPq1VtKffrsoV69doNVYxY0a7+k+vVfx/vrc3ryxHbHA6sdkgyCgBMhIAYQJ7pZ0lVBIDkQyJEjExaXP9M//+SjqlXT0hI42jVsSBDGI2iEvBRIZy+HwYOJevQg+vtvdT1j48e8ebkg/jVYoj/UQZckN/Ncp0mTm7ZvT3JK8YFnzwgelX70zTey0FcMmmQUBAQBQUAQEAQEAUUIHD2qH62Ou7sb6EZuKmqXtdJmzhwIB492mMv6w1vWjSLMsJRA+oDmz/egd97xx78uNG7cNzJHVYSyZEopBAoWLESXL8fo1vz580TFihXTrT6pSBBI7Qjs27cHBgiF1nUrYHA9+/ZZNoDMnv0rxNbfgmbITuzVBMDwEWnQZGUDTKlSRO++S4gCCYOTagC9/vpKaty4CvZptlhpWU4LAq6DgIigu869lisVBBQjkCtXFrwsp9KUKSvBR7kKol4RtGVLJI0aRRDqIvLEk4PpAQoWJGrZ0kiFtXUrcyUTZcxovhmm1OrbNw0WlMXpr7+GUoYMvuYzyxnFCMyYMYgaNfoC+h0hlC2b4mIvMv74ozc8Rd4WmocXiMgHQUAQEAQEAUFAENADgadPI0HrEa1HVYY6wsIi6PTp64o1y9gI8uWX72FjqBZNm7ac6tTZhfmrGzaLIsnH5zmFhHiivjR086Ybvf9+Pdq8+T3iebAkQcDREfDy8qJMmbJj7AZQ3ry29RZsOnCoSo/fhI9tFUlpQcCFEDh1ai8M5vAk1CGVLUs0ceJRszWNGTOEzpyZi3dUCCK1zGYznOC9mrZtn9Obbz6ijz/+DNR2E8DugU0bSYKAiyMgBhAXHwBy+YKAOQQ8PDwQ3fEBdev2HmiwDtCePQfp+vWj4FF+RBs3xmKC/LIkC0zOnUuYABCMJUQ1axq9EDgPU1CeOkUwoBAdO+ZBo0d/jZDNt8Sr7iV8Nn/Kly8HuEd704cfjqWFC0OxcFdWJZgkMNHyokuXitDy5Z2VFZJcgoAgIAgIAoKAICAIKETg6dMocKPrR9PDxpSnT82EcVjoU9682RHt2p2YFuvKlbuYm14DPVaEgfrziy8KUoECOWVuagE/OeWYCLRp8yXNmdOfhg4Nt6mDCxakxfrsc5vqkMKCgKshEBT0GEZIfa46c2aioKAQk5WtW7eaDh+eB0aOEBgqTWYxeZD3BFasCILxoxcVLVoSYuvFTeaTg4KAqyAgBhBXudNynYKARgTSpPGk5s1rGP5xFbNmrYGH3AKaMSOMXn3VWClsJeBLJurYkWAoIbygjbRYT58SFpYEzz83LDYz4/gUyp5dp1mCxutJrcWaNKlGHh79EZEzlnr2DIWQ/XOLYmw3brDApy/ly1eBli3rg8mUvA5S69iQ6xIEBAFBQBAQBFIKAR8fb0QNw+NCp+TllQber9r1yjgipEiR3IZ/OnVJqhEEUgyBFi1agQJnFGhxwil/fm3duH2b6M8//eFZ3kZbBVJKEHBRBLy905qlVVQLCe+beHt7JSkWEBAAFo4+tGZNsCrjR1xFbKCZMCHQQHX911/7sT8gKghx2Mhf10NARr/r3XO5YkHAJgS6dGlGAwb0p9atM9HkyZ7EYpFxiQ0hdeoQRLgI1FkE0Umix499QXVVHVyUM8X4EQeUnf6+/XZF8IHOpAMHqlPt2v40fnwag0g9h7WzBtq5cwT+a0IorD917pwT96cvTZ8+gHgzQZIgIAgIAoKAICAICAJ6I5A2rRc2dfSbZ/j6eiumv9L7WqQ+QcDREEgHbuIff5wBmrcMmjZio6KIundPD92AX8iflZglCQKCgGIEihWriPW1PhGOvE4vVixphMbvv/+GtXsw5cihuFtJMlaqRDCQPqCdO3cmOScHBAFXQkBcfl3pbsu1CgI6IdCgQSUIpM+CsOSfoL3agEiQGCpbNpKyZo2kZ8/c6OJFX9BdERUunB/Gko+xGf+aTi1LNdYQyJo1A6JzBsLwFEyrV++hdeuOgeLqJkVHP8f9yUDly5eBVksVqly5uFA9WANTzgsCgoAgIAgIAoKAzQhUqVICc5K9NtfDFTB9Z7Fi/w9B1qVGqUQQcG4EatWqDUq3ryCMPBl0WMGG6HslVxQO1qyuXdODurgLokgaKCkieQQBQSAeAhUqVAUNeEawYATEO6rt4/79Xlin101SeMWKebR+PSyVNqbOnYNo6tSJ9MYbb9hYkxQXBJwXAX3Mlc57/dJzQcAiAlFRm/SL2TfRkqenu2ETOharuehoqIo7YeK+X716FwaPy4ZN9zRpPMAxmQcGkYLwJIonFOKE1+YMXU4NY8gZcE6tfZTxk1rvbPJdl4yh5MM6NbYk4yc13tWk13Tw4HmIkA+igIDQpCdVHGEtkU8/fYd++eUrQykZPyrA0zmrh0codP7qolZ3GKXcDIYpnZtIlupS0xhauHAWIrtH0YgRAdjkJKwxTUPIRkSmLB4wIBOMJj1BY2z8PZnOLUctIZCaxo+l65RzphGIiYmBo2cpOBzeI9bw0JpQDdWqlQk0V4coS5YsL6q5f/8+WBtqQI/18YtjWj9wG1WqZAZTxOUUdYLMkiWTmSeT1iuTcoKAcgQkAkQ5VpJTEBAETCDghtl1oUKvGP6ZOC2HBAFBQBAQBAQBQUAQEARcGIFKlYqBfiMnDCCXbUIhY0Y/6tv3Q5vqkMK2I+DpGQyx3rcMFTmz8cN2JByrhnbtulCNGvVAZ9UDRpDT1KJFCFWoEA29P2M/b90iOnrU06D3kSNHcZo9exK0cIo41kVIbwQBJ0KA9TQ6dvyKxo4dBSq6MM09X7LEE8aJNxIYP7iys2fPUKlSzzTXG78gS39kz+5GDx8+xN/s8U/JZ0HAZRAQA4jL3Gq5UEFAEBAEBAFBQBAQBAQBQUAQEASSFwF2llmyZCBVr/41NMlCNDXu75+OBg1qS3nyZNNUXgrph0BoaBWnjvrQDwnHq6lAgQI0b95aunPnDm3ZspGWL99Ft29fx/2Kpdy58yJCvw5oshrid5TX8TovPRIEnBCBDh26UrNmC6Gvcc6ghar2Em7eJPr118y0adOEJEVDQkKhpRqd5LjWA+nTu1FYmHZDjdZ2pZwg4CgIiAHEUe6E9EMQEAQEAUFAEBAEBAFBQBAQBASBVIhAwYK5aNasb6lTp/EUGKiOCouNH02aVIXQc7NUiIxckiCgPwK5c+eGZ3oXVMz/JAkCgoC9EPDw8KBp05bShx/Wp59/vg+dTeUtcVRWmzYZUW4BZcqUKQkluo9POhgs9NuyDQuLpbRp0yrvoOQUBFIZAvr9mlIZMHI5goCrIxAV9Qx8kwdo375DdPr0JQoNjSBf37QIwyxM1apVBJdzVfL29nJ1mOT6BQFBQBAQBAQBQUAQEAQUINCkSTVEggygTz4ZY4gEiY5+brUU01516dKIRo7slCK85efO3aRdu07QyZOnYLgJxuaRNxUvXgx0JaUhHl2amAJFkiAgCAgCgoDrIsARVYsWbYKBvzm98859+uabCEqTxjwerMOzfLknTZxoNH5Ur17dEKWVuETx4sURHWKhosQFLHznNu/ejaWcOXNayCWnBIHUjYAI0KTu+ytXZyMCriiCzovRSZOWIjx6Db355jOEcj6l0qUJguYEIwjRmTOEhWA62ro1DYTzGtO337bFC15sqTYONc3FRXxPM3RSEAjI+JFhYCsCMoZsRdC1y8v4cc37HxAQQn36zKT16/dTREQUhYdHJgDC09OD0qf3IY4a+fXXr6hcOdM6BfYcP3//fRRGlxkwcDyht94Kp9deew4PXaKnT4nOnXOjf//1pVOnvOizz96jzz9vTuwF7CopIqIEPJXTO63wefz7ZM8xFL8d+Zw6EZDxkzrvq9arioyMpHHjhkIUfRm9995T7KVEGPZROOgiGkxWFy8S7d7tCUcAfziVVqfhwydDjyOrwbjPNHXR0VAqj5f4WLVqRWn79kdwRI13QsPHs2cJ+kAVaOnSbRpKmy7yFC/EtWtX09696+EwewrvxwiDU0DBggWgP1SHWrVqS6++midBYRFBTwCHfElmBMQAksyAS3POhYCrGUBu3XqIMMwB8Gp7iIUpR3yYv1+8AJwwwRt8l1ng8TCKChQQbwLzaNnvjEy87YetK9Qs48cV7rJ9r1HGkH3xTe21y/hJ7XfY8vWFhITTxo2HaMeOEzAq3MDmz3PKlSsLvfHG61S/fgUqVOgVixXYY/xERkZRz54T6NKlQzR6dBgVK2a+CwEBROPHp6Vjx7LT/PnDDULv5nOnnjNiAEk991KuxDYE7PEMsq1HUtoREAgODqZVq5bBUL4Z77Zz9OxZFAwDHlS4cEGqWPENev/9j19EYlgbQ6NGDYQRYQYiJ61HTFq69t69/al27Slg8WhqKZuic8+fPwft1wT6/ffpiHiJxPs6jMqUMTrMxhl6/v3XA3tE6eHIUJ5++OFXypEjh6FuMYAoglgy2QkBMYDYCVipNnUg4EoGEDZ+vPvuNzRq1GO8HJXfv/37iXr1ykSrV08UI4hy2HTLaW3SpFtDUpHDIBATE4ONmTsQtXxo8L7MnTsrFS2aW5P3qYwfh7mtTtsRGUNOe+scouMyfhziNjhtJ/QeP2z8eO+9vlS16lXq0SMKXrnKoNmzh6hfv0zY8Bpv1WijrEbHziUGEMe+P9K75ENA72dQ8vVcWnIUBKyNobt37yKSogZE0gMQGamt1xcuEH366av099/HEP1vG3PHgwcPqEOHZlSp0i367rswSpfOfJ+YdmvdOnfsL2WEEWQa1atXn8QAYh4vOWN/BBRO6+zfEWlBEHBEBFzFAMJW/Hr1ulHv3jeobl31d+Lff4mGDs0ND74ZQoelHj6bSlibNNlUuRR2KAQOHDhHU6cuoUOHzsGDyI3y5jV6At265QGDSCyVL1+UunVrQ9Wrl1Lcbxk/iqGSjGYQkDFkBhg5rAgBGT+KYJJMZhDQe/x888140FzthGNPlJkWzR8+eJCNIDloz57fUr1GnhhAzI8DOeNaCOj9DHIt9ORqGQElY2jevBl08OAPWAeGKjbMx6HLrB3NmmWAAWI5jBYqFNrjKoj399GjRzDGvEkDB95B1EdCuq542ZJ8vH+fIBKfkQYMmE4ffPCh7EEnQUgOJBcCMviSC2lpxykRsLcB5NmzZ+BN/Pf/QuOXwZsYRX5+6ahs2WJUo0YlatiwEl6K6jmFnz2LpitX7oJT2ShczhQClur55ZcVyL8IL8YIzfdp+PC0lC1bK1BntdNchx4Fr169SytWbKPDh0/Q5ct3ib3ls2b1p9dfL05vv13LQKmQmgQrlUya9MBV6kg5BJ48CYGY3lh68OAsde8eCl0eSjL5ZQ8b9kCdMsWPMmYsDh2fPhj3Gax2WsaPVYgkgxUEZAxZAUhOW0RAxo9FeOSkFQT0HD979pymwYOHIKI5DFQlVho2c3r8eC/QeL1qG+nSAABAAElEQVRDI0Z8biZH6jhsqwEkGhwpUVFR5OXlZbM3sq2I6jmGbO2LlHc+BGT8ON89c7QeKxlDrAXyzTedyMdnK7SpwhD1r+wqWL+1Q4f02AP5FnpVPZQVMpOL+9CuXRPQd+2nFi2UGz/iqmMjSLNmmaGD8kT2oONAkb/JjoAMvmSHXBp0JgTsZQDhEPvRo+djo34rNuSjqGbNCCpZkmD8IAoKIjp5kuiff3xgGPGCR/f7EFdsYRCUsoQdi0guX/4PzZv3Bz16FAAPcXe8JGMhXO4G40YMeBezUKdOrRDaXzuBZxpHf7z+elvavDlQc1gl9yssjBBFkoGOHl2IBU0aS121y7nr1+9BkH0sPXx4E54FoVS5ciwVgWYmTxAQqUknThDENn3RP294LXTBy/sNg+CYXTqTjJUqmTQlY3ekKZ0RuHbtHrVs2Zu+/DIA4/p5EsNH4ubYELJqlQdNnJiRVq4ci+dA8vOnJ+6TfE/dCMgzKHXfX61Xd+/eEzpy5BKi025hQ/gZRD6zYK5RGHOdfAkcMmT8aEVYyjECeo6fd975igYNugSxc+3Ywq8JNLL+mL/PJ39/H+0VOXhJLQaQY8eO0cKFU/BcOADjB9OmuMPxKwZrBl+I5VbGxhqL3ZdL9ivXcwwle+elwRRHQMZPit8Cp++A0jHETp0jRvRFJMhKrPOCLOpTMSi7dhEoqjJiH2kgnq9dbMZp7dpVoLPqSbNmBWuua/16d2rfPkb2oDUjKAVtRUAGn60ISvlUjYBaA0hgYCjt33+Ojh8/R48fPwYdlBe4+QtD7KoYlSqVz7Dhfv78LTz4B0EwKpC++ioSxgjzEEI/C1b+dHThQi6ISI2EESOTycz//HMcgo1j6K23wqljxwjKly9ptitXCMaRtBAt96NffumPCJPShkxbthyG4eRH+vlnWDBsTP36+VCDBn2oadNqNtakrviSJVthUJqJCJYQCGdaLvvwIVMU+MIwUpzmzBmMBZiFG2C5Koc4q3TS5BCdlU6oQoCfJ2+88TmNHfsIfOSqiiICiuAplIW2b59OmTP7my0s48csNA59goWC16/fj4XIVhh3r1BERKTBOJYuXVrDJnPz5g3wLK6oSRdG7YXLGFKLWOrNz96Ba9fuQyTaQkSgPsLc5zkcEcKxSc2OCGkwVnk+4wZjbn1Es7UG1ZC/rhvYqRdZuTJzCOj1/Llx4z42iLpDkD3EXFOKj48Z4wWKys+oS5cmiss4W0Y1BpAbN27AQakjDB3XsUYJgNMXka/vyytmB6q9e4nmzs2Ed1k+bOzNwzrGxELmZRFdP+k1hnTtlFTmNAiktvFz7949On36FAUEBOA364V3eBHsZRTDu9o23QinuaEp0FG1Y2j//n9BPf41Iv4fU/PmAZj3E0TSyaALyfs9hw+7w8k2A7Q2ihh0NwoWLKjLVTVqVAnC55eh+WpbdZkzk+xB2wahlLYBARl8NoAnRVM/AkoNIJcv/4cN+FmwyJ+C51cMvMfCQAdF8HAiLPbT4Hg6RCakgyd3Axgy1mBzIAh8/crx27zZjX78MRv99ddkeFFmTFBwwoTF2HD4A3WGUf78CU6Z/HLpEmHjwRcLvTb0xRfv0fDhs7BQWwk+R5PZVR1ct47o1KkmELrqrqqcLZkXLNiIRdMs4BqmKoJlxow0tHVrAVqzZlyCiBhb+pISZdVOmlKij9KmNgQ6dx4OrtYD+K0atT7U1rJ8uQd0eSrQggXDzRbVOn5u3XoIz6KTMPaeRMTZExh702CBVAT9LYnNjdLJsvFu9qJS+YlVq3Zh4TONqlWLwnM7jMqUMUYP8mWz0Zyj3Vau9IWXbVo8i7+Gsb2KXRHROobs2ikXqTw4OIxu3HiARW8s5cyZOcn8IDlhuH8/AHOKkXifXoN+QhgVL2669RDsLy9Z4on3th/mTT1Ah1DD4BzC1xAdrZ5SwXQrctRVENDr+bNkyT905sxE6t9fvfZHYqwPHSKaP78SnI5GJD6Var4rNYBs2bKRhg37Eu+iAEUagzt2EDjiM9H33/8CGuDGyYKXXmMoWTorjTgcAqlh/ITgxbx48Rz8+w1r6QjsYzyDg0IkPXvmQefPp6Nz52LgOFkHFEp9qEQJUFZI0hUBrWPo+PHjtG3beqzF9tB98Eu5u7vBeJwfVOq1qEmTlroaki9hA6l//4ZgGXhi87WLAcRmCKUCGxAQA4gN4EnR1I+ANQMIL9h//XUlQgGXIWw+FPyKRsolU8iwRb5FCzcsimIR4m0qh+VjGza4YyOzCLx+fzZsFnDuWbPWgKt4PuoMtxhJkrjm8HDCpqovNh7a0B9/bKaffrqJCU3iXOq/X7vGC5ei8EqerL6whhKnT18Hr2VvtBcKqgH1Ffz0E3M114dxKfkMNup7abmE1kmT5VrlbEojcOzYZerduz9+3+rF7uL6jscTqN78EC49EoaJYnGHE/xVO36OH78Co+lU+u+/m3jeRWKSHWUw9jLtB3t2HzzINHOe9Mkn79LXX7dOETq8BBeYir6wtlPXriMpMPAkjRsXhs1uyxd3+zbB69aXChWqggi/XnYzSqkdQ5Z7LWetIcDGx1mzVsHxYRcMj1FwfHDHojcWv0miJ6BVrlWrLMbJB/AILGStKt3O37z5AJGfPalv3wD8VWbE4P527epLbdu2h+GkpcGIIwYQ3W6Jy1Sk1/Pnu+9+wW9mAzXWYc+dBWcbNswKI/TCVHsflBhAduzYjvlCZ1q2LNDq+yo+UByp3bp1BhhBZiMKtl78U3b5rNcYskvnpFKHR8DZx8/OnTuwdv8cFNmBmLtHQj8wKeRgy6YtW4gmTMgEJ6dWYFIYgX0H52ZQSHqVKXfEGcbQ0qVL6PbtntB6jbQZKDGA2AyhVGADAhLLZgN4UtS1EWDjR79+v+BlsAP6GeEJwrlNIbN0KSH0W5vxg+tr3DgG7dyCEWQTJiiNwKt9B1EfvxvaVjsH8QEt8bx5YfAgZk8PN4P2iKk+qz3GIe1Pn9r+YlTSLuPfrdtIGj9em/GD2+jVKwq47qBjxxrCKFVYSbOSRxBIFgSmT1+G8a3d+MGddIOLA9cxdepieFsPs6nfzDs7cuQcGGA34W8oVUFQAdcfP1WtGotnU6ghCmHKlOWIhttmaLdEibzxs8lnDQgw/u3aDQbf7xkI3BvprqxVw+Hwy5aF0ZAh++C1F4lN80EvjOfWysp5x0OADWBjx/6O6J4N1LnzU1D1PE/y7oauMCIbd+HddhSRnWWwWfGtgWbKnlfD7/z33usDR4rH8BBV3tIrkCfi8fnBBwvQ11fsHqmkvGeS0xURCA4OAZ2IPleeLh3Bcxo/RhdOTJ/Dm6orV6ozfjBkHEG/aFEQouY/pw0bDoDGM7MLIymXLgjYD4EFC2YiInMU/gWZpM+Oa5n1NBs1Ijg+BWBe8Tve2wfwG/0Lex9Y+EtyCQQuXDgCZ7rk2eNxCUDlIlMMAfcUa1kaFgScHIEZM1bTzZs7aPp068YP5rddu5YgZGzbRQ8aFA79jsUGT8l+/SYaNiK1zj3SpycYD7g/sYYNS9t6ZizN9Cu+vmn1qMpqHXv3ngHlRwCEE61mNZuB6Uz79QvFhuICs3nkhCCQ3AiwcW/PnhPQ9LG95bp1iQ4cOGt4ZmitjTffP//8R7pzZz02XUMNeiSJjR/x6+Zny4ABkTRmzF0sknrTyZNX45+WzxoQmDJlJTbnzsHDXpnxI64Jvk/DhkWAV/2YwXged1z+OhcCTHX1zjs98K5eAwNHKAyNSY0ffEX8TuNNCo4cq1btoEFDiJ0l7JmGDJmB6NYnqowfcf1hZ4zp08MQ7TYefOPgxpIkCKQQAulgteDoaD0SR0S6u7v2EnvkyL7QOQyg3Lm1IcoG0h49ArHO6autAiklCAgCFhHYuHE9dCJGggnCsvEjfiU8x+jbNwK6E+fhWPO+TWuL+PXKZ8dHIDIynNImzxaP44MhPXRqBFx7dubUt046n5IIXL9+DyJQiyHUF45FjvWebNhA4GIk0MFYz2spB1OeFCoUAd2KfaC7uAEPa0u5rZ9r0IAoNJSwQWk9r5Icp04ReEOThxt03rw/ICaPztuYeIP4yJGzFBYWYWNNUlwQ0AeB//57DOOeeTo9Na3w8ylvXje6du2emmIJ8k6cuBSLnMPwPo9Q9QxjnaNZs4IRuTCQgoJgBZakCYE7dx7RnDkrQCUCXhUNiY0gP/0UDtqsuaBIkk1mDRCmaBEWvG/Vqh/o7G6AajNCEd0l3/M2bZ7DuP8YZfuAGzrALtdw794T+vvv3XDuwI6vxsQbpPwuZzpRSYJASiFQqlQJaIBgd0+HdP48QQMHIXgumoKCgmjfvr/hAGFbFEzr1tH077/bQfsY6KJIymULAvZB4PHjx5hT9qTZs4OtMliY6kHHjlGILj0NzZB5pk7LsVSIQIYM2fAsToUXJpfkcggo2Lp1OUzkggUBqwiMH78AHIjKqZcOHiTwclutVlGGmjVDMeHYjNDwsCQUNIoqiJeJN0neeMPI6xnvsOaPO3f6ghvUhpAMFS0fOXLRQMOjoojJrLxBXK6cm3ipm0RHDqYEArzh/eqrEPDQKeXJw/oAjzXVdvHibYS5r4KA6VNNz5uSsId27RoCj7GfNbUvhdiI9Cd16WI90tASVkzt8tFHT6EXBWu8JKdCYOLExRC6vw2DhvrNRI6Q7NcvEAaK0Xbx1Jw7dy2MF0+hL2MbpHxtixdvpOdMNC5JEEgBBKpUKUF79yIkSYe0d68H5qfJMxfWobu6V7Fx41/QArL9ucDPlWbNwhF5+pfufZQKBQFXRuCXX36E9lYA5cihHYVRo0Jo8uSRFBkptEjaUXSekqVLV8ZeiVCeOc8dk56aQ0AMIOaQkeOCgBkEOFLgn38OIaJD+Qbl2bNEpUqZqVDl4VKlYunKlYtUvrwyoVFr1TdrRnT4MNGjR9ZyWj4Phy8IIHuBtkeDwrvlqpOc5U2S588jiXmW9UglS4bR2bM39KhK6hAEbEbADZbJ2FhYJ3VKLIbOxk4tafz4+fTdd6E2/dbat4+m48ePQy/poZYuuHyZP/7YDi9+2zeG27Z9Bp5n2UhypgH14EEgLVy4Fr9B7RGKLEoeG3uZtm8/pvulb9u2D9Rc6g0ziTvCtHkFC8bSxYv2petK3K58FwTiEChZMh+FhPjT9etxR7T95fftkiU+MFjW11aBE5Ty9AzGHNzfbE+PH98Jrnjtz6z4FTPnPNcnSRAQBPRB4MGDB2CSWIYILdvmlRkysCZIBLQB1+jTManFoRGoXLky7dzphfmkQ3dTOicIWEVADCBWIZIMgkBCBI4evUR4Bxi4thOeMf/tKZhL/PzMn1dzhusJCAim/PnVlDKft2BBoixZmCfefB4lZ374IR28Sd6HJ6iNrqAKGouKigYNiMYdXRP1+/jEgPtZn8WaierlkCCgCoG8ebPbvAkTv8Fr19xAg6XezSs09CloLI5TfRv3cdj48vHH4RBEhxCSJFUIPHoURP7+0bq8P/g5Hx39VJ51qu5AymY2RljYzrvcvXsYTZ26WNeLYa2iu3cDKFcufap97bWndOzYZX0qk1oEAQ0IfPvtJ6CFsc3DdckSD2jTlYVndSYNPXD8IhERJUBp+ZbBScPcRtjVqxeoSBF9roXruXbtgj6VSS2CgAsjwIaPXr06Y05fEU6UobroOTRrFkbbtq10YVRd59KzZcuGvacyoCW07ZpZI0uSIJCSCIgBJCXRl7adEoHjxy9C50Kd9gQLlbNAuB6J63F31yf6g/vDm5OsLXIPEgFrNDpxbNrkRpcvvwKqm+Z6XKLVOry90xAblfRKgYGelCGDThYqvTol9bgsArxx8vixG0VF2Q5BNJyz+bf96qtZVVfGxt4qVWJtprfhhuvVew5h98Oq++DqBS5cuAUuef1QKFzYHc/q//SrUGqyKwKrV2+H9odtXprcwYoVCdEV1ykiQoeHyv+v+NmzaJsiwxIDlz17FCJRhWA6MS7yPfkQaNGiFsXEFIIwsLblMUePTJniD82lHsnX6RRoiSNUzRk/uDvPn0frMm/gutininWQJAkCgoB2BDZsWAM6uepUrdoaAy1t9era64pfskwZgnbS6fiH5HMqRqBnzxH0/fcZyRYjxsSJOtF3pGKc5dLsi4C2GZ59+yS1CwIOjQCL8WXKpC7+r3RpIhYI1yNxPTDC061betRGdPMmEYuQTp9O9DNo+v/8U12969cT9ejhRj/80B2GmeR5pHA7/v6+uolxnTrlQ2XLFlJ34ZJbELAjAvXrV6YNG2yPctq8mahu3fIwdKqv68SJS1S6tD7i5a++SqDA0qZDYkeYHb5qjnbz8lL3vrF0Ud7esVi42E5ZZKkNOacPAlFRzygsLIwyZ7a9Pv75lynjTqdPX7O9sv/X4OHhjo1J3aqDwdcNkbX6iFDr1yupyZUQ4PfkzJnfYz6cndatUzefvXqVqF07f5oxYzDWCObpoVwBz6xZsxOczXVJXA97HksSBAQBbQgsXbqAfv31a1BVPTY4VED/XLfITaaijo7Wz7FC2xVKqeRC4LXXXgPVeVsaOtTXohHcXH/27SNoOqlnJDBXnxwXBLQgoG52p6UFKSMIpDIEvLy8IPil7qKqVCHasUNdGXO5d+4kqlqVQBVhLoe646Dmx+a/kQbrjz+Yu5ggmEpWFy+sGdIDTm6zZxNE0GLos89GwNP8ibrGbchds2Y5+vtv9Zu6iZuMAPPV2bOxVLJk3sSn5LsgkGIIdO3aGpswfvCk1N6FGASKTZ3qR59//oGmSoIRbpYxoz6b77wB6+6uT12aLkanQkz7s3v3KdCkzKIWLXrCuNQFHMhd8cz8kebN2wSjbKhOLRmr4Y20J0/0m6pxZJGrb87peoPsWNn9+4H0yiv63fv8+SPpxg2ddiVx3Ux3GRvrqUukGsN46ZIPFS0KS6kkQSAFEcic2R/OB5NA2ViQevdOZzV6m9/R8+Z5guYxE4wfw+FhXTIFe+8YTZctWwu6Hfo8u44fd0PUfS3HuDDphSDgZAgcw2bBzJmDadmyYMqa1dh59lW0ZW2RGAJL0WCJ88p350egT59h2COqjkgQX1WRINu2uVGfPjlp/nyNdCPOD51cgYMgoM/sxEEuRrohCCQHAsWLF6Lz59WF7zVqRLR1K9lM23T7tpHOpmNHolWrbL9anrSsXs0iZsa62NN02TKiWlhrtGpF1Lkz0YIFRmPL5cuEBY3xe9euRM2bG2k1uB8NGhBEWgOpW7cfsSGSPJucXbq0xKLTT5MHQnzk1qxxg6B9LUqTRjxP4+Min1MWgeLF88DQWR1GkDSaOzJ7dhpsHFSG53cBTXWkTesDvQhNRU0WYoOMM6c//9wFKqH28KQbBk2VlZj8n8Pz8TaMTDeoUaOddOXKVDw7O9DXX48BR7o+kTMlSuShc+f0eabyo/n69RgqUCCnM98Gl+k7v0s1BG6ZxYc3PfR+P5cpk59OnDDbpKoTR464U7lyhVWVkcyCgD0QyJo1A23aNBnOQR+DLz8DNm18aMsWguYNGRyggoLIwIM+YUIaql3bH/RytWnXrlmgjCxhj+44XZ1vvFEf3uYZdek311O37v8XKbrUKJUIAq6BwDPwFPXq1QFz1iBKn/7lNefLR8QRa3qkJ/B79PePV7kelUodVhHge3v48GE4oc4EA0d/GjnyOxgW5mA+dgLGLRs856y2bHR+mTZtCShQ22Ptk5EOHOC5pfmC/N789ls/0EO+hj2mbZQnT17zmeWMIJAMCMiOXzKALE2kLgQqVixKY8fypqRyEYq0aYk++oho3DjCppl2PAYNIvrqKyIWLvdHhD2/dDi6RGviaBKmpmEKrLjEGy4ffkj0AZzGuf6DB430WGDiMKRr1wjXb4xC4Q2VuNSkSQzos67Cc+4ADAoIUbFzKlYsD0Qmi8CAcxye2Np2VllPZdIkP/QZN0eSIOBgCIwY8QW9+eYpKlz4LoyMFmaXJvrN0VFLlmSh7dvxwNCYSpYsQH/95YPStltBOOSePWudMYWHR4AzeSSFhp4FpmHwyk96FSwEXbduFDyGo2jRol3YFDuGRecAqlmzTNLMKo54e3tRzpxZ6cKFECpWTEVBE1mPHiXoieTVRIdmojo5ZGcEsmfPiKhKbe82U127edOLGjf+vwuoqQwajn30UTOM94tUqZJtBj82ouTI8QplySIbKRpugxSxAwJMtfrFFy2pS5emeA8ehIFjP57p52Hcfkre3p5UokQ+qly5AqK760pUXSL8S5QoCUNRdhjvnwCnRCdVfD1/nh3HslOpUuARliQICAKqENi4cQOcdgLAcJCwWLlyRD/+mPCY1m9HjjCLRAWtxaWcSgRCQkJo2rTxcIJdBNxj4DQSjH/RBgPEtWtpQGXOBvk02HPqjHfXVxC6xwaUHRJHAA8YMIreeac1jR8/AM4B5+mNNyLRp3DM4wh6c4TnvyccBfxBf+yHvnyLvaP2yUaVbodLlipTEQLY6pQkCAgC5hCIitpkctexQYNuNGLE1SSTCnP18HHmyuZIkCFDCJtilnKaPrd4MRHz+c+bZxQuP3eOQG1jPKbl/cYGjfr1iX7/3WhQMd1qwqOs93HoENGwYQmPx31jfZIRI4pgsfhL3CG7/n3wIBDiyl2xARNkMAqpaYy90T/91AfRLx3xYn5XTVGHyuvp6W7Y0GTP3uho/TbLHOoiXbgz9+8HINqqF7377kPQLD2zKizKjj8zZqShP/7IAuPgePD8YiZqIVkaPw8fBmLD9FNQzYXY7Im+di3RkSP1MVH+1kJvHO8Ui0Y3a9YLBqjreF48U4wDayu1a+eH6x2ERcHrNl3YihU76J9/JtGYMcqN7qYa/PxzX2rf/js89yuaOq35mKUxpLlSKWhAoHLl9vgdP0jgvakVmlq1/GnPnt/J11e/BTF7Glaq9Ak8Dx8hskhbz9hzsH17X+refSDe5+W1VSKlXBYBef4k762PiCiBuWZ6ix6/3KMDB/aDKvJDaKkEWZ23mLoCnss0bZqRBg5cBFoxnRSbTTWEYzKGzAAjhxUh4Kjjp1WruogMOAHHl4SXwe/c2rWNTBK2yut07pyROnZcSDVq1EjYiHxThYCSMbRnz27q3/8zatv2CeZMUeTnZ7qJgACi335LC+fOrDCI/E6vv27bGsR0KwmP/vfff4Zn/smT/4I28iG0C9OB0rQClS9fCUwEryVxvMqSJZPsQSeEUL4lIwLx/LeTsVVpShBwcgR69PgE3hPqBKBY23PuXKJ+/dTrgbAuBxs+foFdIY4Sg72qWrcm0E4ZjStqII2CXhnTWHXpotz4wfXv3094mZlvqQycnR8+vKc7D765FtlDdtasoeBe9oeOh7lcSY8/xT7il1+mQxhmbdB8NUmaQY4IAg6CQI4cmRDFMQ2/qbexCe9PK1a4GTxrEnePdYn++MMNRlY/eI2/iQ3z6VaNH4nrSPw9W7aMiBDLSxw5YGtasMCfPvmkua3VJHv5/v1/wULxOjSOlBs/uJN58xItXhwKOqxRxEYsW1Lz5jWh+ZTJJqohFh68cycbxAstPMBt6aSUtQsCjRrVhAHEw+a6T59m0dOcuho/uFPsBThhQh/ogfkanDy0dHTFCg/0qwSeb/oa5rT0RcoIAoKAPghUqVIV3uct4DClbq3ErfMG7fDhvvBsbmZ344c+Vyu1CAKOhQA7J9y+fTOJ8YN7yfsI7dsTItps6/PFi0yllZ6qV7evgdK2XqaO0mvXroID7cdYV9zDvo954wdfbaZMRH37RiBS5DZYQ1pCt3Cn3UF4BaHxLVq0RB/HwvFrHvbIpsEw1gURIWWTGD/s3hlpQBCwgoAYQKwAJKcFAVMIvPNOFXAfFgeXobqNCaabYo2Nn34yCogHBpqq/eUx5k1kHY6//iJauZKSeIGy8aMwKLOZXuvevZflLH1iHRGmt6pQgfByspQz4TneYGUdE44asZTKlYuFVsgVS1l0Pcecy/Pnj4ZBIzteul6E6FCziRdVTPvVsKEfDDmtEY7ZQ17MZtGSE46CQLp03vD+/5oWLpyEkOKG8JLOhI3s9NSpUwY8H9Ijiik9ogwy0cmT9WFknYgNyZ7k45NWl+5/+21HRHX5kS36Hfyb8/DIBS+ggrr0Kbkq2bfvDIwO/2IB8UxTk/y8798/lL75BpyBNiTWJ5o+fRD64UdMJaY28buhTx9/CGF+L+HnasFL4fyfftqC5sxJp0po0lSXp071hdMDJgp2SHXrloWRsCHewT6qjSBM1TdtWhbo6Hxnh55JlYKAIJCSCAwePA5rk1rQIfBVrCfGDkp9+viCUqUGDR06PiW7L20LAk6LwPXr1xGVaX6br0MHQkQoaXZwYlaLHj0yYKN7hqyj7TxKWNdjwoRecHILItZvUZqY+mzZsgA43naE/t91pcUknyCQ6hFwS/VXKBcoCNiAgDkKLK7yyZMQbKR/BSv7A/xVTj3EG/Rt2/qATz4DvLrvwrvJKDpeqhRh05LAL0zEVFLbtxPduGHU/GjZ8mXkh6nL2bCBBabcsMGRBtEQUWQqpJU3wVjQ/M8/2bPqpfC5qfpMHePoE+4b65BYSlOmuFP27F9hcxZ8X8mYmKpm4sTFoMPaCF2U5/A8C6MiRWINofcPHxI2Mr1BIeaFY4Vp8OAvwEsMF+1UkJSEzaaCy5RLSIRAQEAIvLsewVMyFlEa2TTraygZP19++RPlz78HfOjqDQEskPjuu/7wYv/F6cS3W7bsBVqeM+B5TwS+yq+NGvnDSDsZNH25VJZMmH316t1YbE6C6GEo7kfCc+a+sYdely5+NHp0f92pr+LaVDKG4vLKX/UIDBw4FZoDm/COR+imhvTPP+zpWYQ2bpxst40Kfg4NHz4LegiboasVapWSEvqdWNB707ZtWWjt2vHQ1clqeJYJjaOGG+ziReT5k7wDQCkFVlyvYuA9MXXqeFq+fCo0soLAGR9LaVhGMVHiZwI7e40bl5Hef/8LeDn3MkSYJcpml68yhuwCa6qulCMsokCn4O3tDbofT8O71ZGoiHnTfOrUlnB8wSTcTGKNHXaEZCpsdqZUmtj40b27P+ahn2BzfYTSYpLPAgLmnkE8xho2rAwGkBtUWqMUEjuhjR9fBuuwnXabA1q4NJOnhALLJCxyMJkQcEumdqQZQcApEbBkAOELYg2K1q37IZrgAX333VNEhVi+TKYh6dfPH95Qnem//x4homMhaJiMuhqs6cGiUczpyMYQNoxUBCMEh6paSxzZULlyeiwYWsNLfB02EsINwoN+frGIiHAz0EPdvx8GCpoYeGkaBdSt1Rn/PPfts8+MeiNspLGUpk1zR/hld2y6vWMpm93OPXsWDa+W03T48Fm6dOky8STVz88fG8TZDaKV+fPnwCQiv24e8na7EIUVm5s0KSwu2VwcASXjJywsAlogPRBpdgfGW6x8FKZHjzg6zRdG4h4Ija6tsJRjZONne5Mm+uifLF7sBmNVc2wQg3fQxrRr10nQao2mDz8MxvM8mvz9TVfIxurZs9PQmjUZDdEjlSoVM51Rh6NKxpAOzbhsFZGRUfTmm92oZ8//VDlbMGBsAOvQIQOMDJOwWZHT7hjy+OzZcyyivcLxrAgD9zRBhNPYLM9TWBtn/XpPWrIkHfj934JDRSe8i70dbvPI7kBJA7ohIM8f3aBUVJFaA0hcpVevXoU+2RgYSbdQmTJuWOeEYg0UhTWKF50+7QfHr1iqU6c+HLn6UKFCheKKJctfGUPJArNTN8LGjcOHD8HJ7ldQkh5GVGY43m3uiGyKAYVjejje1cScrDsVK1bCIa7zwoULiN5ujP5aDhs+dozg3MTRygS9QetdZxaJr77KAHq6D/D+Hu0wG+rWe+7YOcw9g1auXIYonV5wfoJwqw2pS5cM0CWcj2dsHRtq0a+oGED0w1JqUo+Agq1V9ZVKCUEgtSBgzQDC18kb7BMmLIGH71p4OkdigyLSYMDgRT8v+O/cYTFAgmHCH0J7OWny5P6IQsgNb8yD+DeaRo0KtxkurCsgFFgUYoOTDXWFhITD6HGDeOPSzy8dxNrz0YULt0CXMxCeHiGqxEovXzZyhU6fTtjUsN7VwYPTgY6nL7CABScFE/Puz569Bl5nWzA5jQYGsZisxlBAgAdohGJxLAOiVN4DHdibMFx5p2BPbWva3KTJtlqltKsgoHT88DOlffvBeJ5chahiOAyK5hHi597GjW70ww/++NcDG501zGd20DMbNhwA5d9P6L/tz2deMPboUQARaNN0udrQ0Kfwsl9KS5duxjM5Bhy7YdAceW5431y/7gEKQl884zwQDdgYXnrv2/35pnQM6XLxLloJG+SaNu0Jz+hH9Omnz0BlZh2IrVvdEO2YHpGfP2Czooj1AjrlYI/vLVuOwMixFrR8V7FRFGno7/Pn7qBvyAYKvxowyjShLFnSG1qU8aMT8C5ajYyf5L3xWg0gcb1kj+Zz8Ko6c+YMNo/DYAD1xZqpFByUSsCT3isuW7L+lTGUrHA7XWOXsRD+9tsOlCHDf4iYCDA4KMZ3BgwONtIrz5mT2UCPPW7cLGhu2RbxaytIzxBSVadOMfr3X+sadA8eMO0cQbyaML8gOFy8dFzgfvCcnp0p5s/3hQHTD4aVKcjzlq1dlPLxEDD3DGratBqEzM/DKBwvs4aPrOE6Y0ZtUKqu0VBa/yJiANEfU6lROQJiAFGOleR0QQSUGEDiYAkPjwDX4naID++FJ9N1cGFHGyYNuXNnht5GaWrTprEh8iAu/6NHQTCWdEH+EEVRHnHlTP2dM8cDC4nW8OD4xNTpF8cOHDiHyc0weBCHYOP/ucV2ecKzaJFRJG3qVMIGyotqLH5o1ow3XKZS7txZLeaz10n20pk9ez0MTQtg4HiKCJ3oJNop3DZ7os6d601//+1PU6Z8BxE3hN04YTI3aXLCS5EupwACasYP/7YWLNiEUOoF8HaLhOB6ODbfyUC5hz0NwwLpwAF3bMz7YrJeBFQWvUBtkyUFrsr2Jn/6aSEi2RbiuW17XfwsrVo1PTZ8ltteWbwaoqOfwxPxMrwSzyPC5I7BEy9v3jx43xSF530hoQ+Jh1Vq+MgODQMHsvfpfhi2Qgx6XNAgT5B4rB0+zAtdP9Bsvgp9jYGIMs2WIE9yf+HnBic3M+Gsap5Byd13ac/xEZDxk7z3yFYDSPL2VllrMoaU4eSKudasWYm5bF/8C8A8zjoCf/3ljmjfjNCYnEc1a9ayXsCOOd55pzL99tsl0OQqa+T0aV4XG502PT2NYtqs/3n/PoHuKw0ErqcgMrqZgfZLWY2SSykCpp5BTyGIVL9+KbBaWDdiWWuHp2GVK2eCQ+4Vs3Mxa3XoeV4MIHqiKXWpRUAMIGoRk/wuhYAaA4gWYNq0+Q6ekMew+a6ltLEMv9Tq1WOO/emKNhtZu6RPn4kIOT+FcMgwiJfGGCJC2KOUhY45moT5IhcuJAPf5AjQe2bMqKx/LNrepk12Onhwfoq8YDkap2vXHyky8giE5p+aNHwkvpJLlwi0YL4wDHWEZ0+TxKcd/rupSZPDd1o66DAIaBk//DvbuPEQdIr2YjP2PPSQwhDd5k5Fi+YGbV85GFfrGXRJHOYiNXRk8ODpVLz4anjdayhsoki1aulhGNfXAGKimRQ5pGUMpUhHU0mjJ05cBa3ZUnhiHkNEhRv0NmJg7IpFtKkHjJCx+B3mBaVFG3rrrfIp8h5WC7OMH7WISf74CMj4iY+G/T+LAcT+GEsLjoHAX3+tRbTt12ASCFK8Duaec9Tvhx9mBDvEUqpUqUqKXczChXOhJToIVFXqI5lDQ40RIRyUtWRJGlxDXzhe9E6xa0ntDZt6jx09ehTOLO9b1HFRg0vz5lng8LkDDqoKLWJqKleZVwwgKgGT7LoiAPuuJEFAEEgpBPr370Kff96PNmwINSkKqKRfCxZ4QoOknCLjB9eXObM/IiQGY4L2EHRYGzAxOgqjxz0InAcjvJcMxpAqmK8tXkwI4VXSg5d5Zs/2RtRFyxTbdOnbdzI48Q9DDD0CfXjZL0ufioAZZNWqMGgVzIXHdwZwoKasx46lvso5QcAREPCA23mTJlUN/xyhP/bog79/egMdgB51s5GajcuSBAE9EChbtiAiOwYgwjSWrl27R9ev38P4iqWcOTPDaJcHxshEYSF6NCp1CAKCgCAgCAgCLoLAXXj0jRjRE+tzdcYPhocjLhYsCAQNaQfatOkQqGMh7pkCqWXLD+AIMRqOluGKo0Diusld5n+s5bdoUXrg0CnulPxNJgSCggJBFRqtW2tZQczx5EmAQxhAdLsoqUgQ0ICAGEA0gCZFBAG9EChfvggmJ2/R6NEb6fvvEWeqMkHjDN4B/rR7dw+VJXmClg3C7R1Qjv8RuMJnwGjwF6JD1PeDy589S/AIz4i+pIz4+d9/H6UTJ/bAmKHc+MH95sSTvDlzwuDtPQXROGUoe3aFIS/G4i/+5w2p/fvPgXP1FOhuzoKWLAIGGV/w9JemGjVeA41Y4Rd55YMgIAg4LgJlyhSBWLMvOmib8CBf4Y0bZNA+cNyrlZ45IwJMKVWwYC7DP2fsv/RZEBAEBAFBQBBwRASGDv2aBgzgDWhtvStYkGAAeQLK2GGgjhqrrRIbS/lAqGT48F8QufEprVgRrNrRkh13evb0p969f4DzpAXhPxv7KcVNI+DpmQaMFqDn0CkxnVlK6SzpdAlSjSCgCwL6/ap06Y5UIgi4HgLDh3elK1eKwwjircpL+NQpAmVTeoiSjUDkBm/U2ZYGDepE27ZlhXipwtCJeM09fEjUrZufgW/c2zv5RQzZ8NC//88QCgtTJA4br+svPmYDTXqPHiEQeP7txTGlH7j9efM2gP7nY4RLD8EEYx61bXsAE8cTEK3dB+7UWZgAfwcjSCdQle1RWq3kEwQEgRRCoFKlYrRvn7tBx8nWLuzY4YHffgVbq5Hy/2PvPMCjKNo4/k8llQChI713kK4gIL1YaIIoRUBB/BAEAQERwQKCCKKg0kEUEBEQpUgTRXrvvfca0nu+/xwGA7lL7m73kivvPM8ld7sz7878dm5vd94mBISAEBACQkAICAEhYEMC169fx4kTu/Hcc9pcd197LRarVv0Clcshs0rjxk1p2NedYZ4DuZhufi+YxhQDBgTQWPIFKE8SKRlPoCRDVJw8qZ9H79mziShSpEjGD0SOKATsjIAoQOzshEh3XI+Al5cnLTPGMmFpPYZf8sfx42kziKBB8uefezOReW66pX6mm1eBt7cXvSc+pyImDxfzPc1e+FNJ0zp0CMSHHw4yJN9Nu/e22btly2FawUbyh12b/HbtEpkUfSfPhfk3qzdu3EPLlv2ZpGwWz+NtzJwZRnfjJCYbA8qVA+rUAd54I5EhxcKYjO4q3aInMU/KCISFWR6TVdvopLUQEALmEggOzorSpUvgn3/MbWG8HnWjzKfkh65dnzNeQbYKASEgBISAELBzAh4e4YiIqI6EhEA776l0TwhoI7By5TI+14abHUrZ1NGyZFE5OuOwadMmU1UyZPvgwaP5TNofzZplw+7d6R/yyBGgRYsghtbuTqPAKZkW1jr9njp3jbx58zIEmTujSWgf582bgI9PVklgrx2lSHACAhICywlOogzB8QkoJcjkye8yqekBDBkyjV4Md3mjEoEqVRIN7rexsTAoRrZv92OIKU8uoDfngvur/CHT19siT57sWLduKsNgTabFx17euEVAJQk/cAC0YIHhZlAt6JUqBfYNuHXLG7t25eDC/vu6KWKsOZsrV25kf5mxTWPxYp63hg0TyPgQb/6owUinXL9+F61a9cfQobepBCGYdEqhQqDHTgSVIAfYbgBjqk5miCy/dFrJbiEgBDKDwJAhPfDWW8NowRfOvArW9WDRIg+UL18RBQvSxcxFigr9t2TJn9i8eSsTv5+j1WEcR+6G4sXzolatqobfr0KFcrsIDRmmEBACQsDxCYSGNuAglFekm9kGUo4/ahmBKxLYt+9PRljQJ/dCjRph2L//Hz4jZk546OTz9+abg1C/fnPm/XwL8fEXqeAJodFiEsOzPni2v3QJYM5tLF2anc/7uWkMOY35RZ9Mbi7/M4lAq1ZtaZw6E6++mqCpBwsWZOE576lJhjQWAs5CwPJYN84ychmHEDCDQGzsmvRXtc2QY6qKp6e7wbJChVCKj//P1fbo0QtcPNqPffsOMGHVfYZU8mJy0xIMsVSROUOeNHw2JVOP7Vu3HmFy9o+ZED2UbrMJvEkCF+ofSFZusSdPqpskN/4oZ0GvXm0ZH/QVqMTImVUaN+6NadMu0FpFew8WLwauX++IESNeS1NYQkICz8Vb9MS5gCZNLJ8m8+Z5UolVCQsXfprmcdLbaWoOpddO9gsBRUDmT9rzYMSIabR4XcuHRgtiB/wr8swZFQM6G6/lM5A9u75Ws4cPn8eKFZtozXcQly7d5oJUEq9/OXitrsDk9PX5W1E67YHpuDd5DsXFxWP8+O+p5F3JPsShadNoVKgA+DNCIy+XBmX69u3u9Fz0p7deSUya9K4hcbeOXRFRDkggef48fh/kgEORLmcCAZk/GQNdeX8kJXk4pfJD5lDGzCFHOUqLFtX5bHaG923ae6yiJEya9AyjA6zQLkwnCSeYQFSF5lJ5My9dumz4TufPn4+GjU+jefMXed9WUacjiRhzCZi6Bl2+fAkvv1yf4cnvgSldrCp374IKuGCsWbOHIdODrJKhd6Pg4OyyBq03VJFnNgGZfGajkoquSCCzFCCZxVotQHz66WysXbuaHinhBk+PtPqivELGjvWldUteWvx+Rm+VrGlVt9m+ihVfZrz+e5rdlVUHt24Fli2ry3wm76fZ3ylTFuP06UXkZX64rJQCiZqhsvxp1dEfbdo8k3KXRe9N3TRZJEQquywBmT9pn3q1qN+x4zA+EJ6kojfG7BxDx46ByuFAzJr1sa7KiAMHzmLQoAm81t2mNVcYLfRARfV/Fnz79inldCAV54GYMGEQYz+XT3uAOuxVc0h5w7VtO5hhFm4xH1M0Xe1NC1bXvt9/d2dohQBDH5s3r2W6suxxegJyDXL6U2zTAcr8sSneh8JFAfIQhbxxcgJNmlTlc+D5h4Z/Woar7gU/++xpzJnzmxYx0tbJCaT1O/b11xNw5cqX9MphDHQLi7rfVs8iTZuOw0svdbawte2qiwLEdmxFcvoEJAdI+oykhhBwGQJjxszE4cO/07I4feWHguLrC4wZE8Uf1wu0+B2A0FDLf5ztDa66WXBzS1s3rBZFv/vuFwwbZp3yQ41ZHeKTTyJ4YzzbYL1tbxykP0JACAAqPOGiRZ8iJKQuF/gDDF4MaXFRcXa7dHFHp06eiI5OYhiF96k86UAlSBdeJ0dj/fq9SEz8z9svLVkp9ynl9MSJP9AjbwhGjTrHh/MwhpICPQPBUIigVyAYYgpo3x60XAzDlClXqYgYRU+2aVYdL+Wx03t/8+Y9Wg2+jYEDLzMXVNrKDyVLXftat07E8uWh+OCDCVi9ekd6h5D9QkAICAEhIASEgBCwOYGcOYNx44Y+h1FygoPz6CNMpLgkgTffHEhPnar0JErDssgIGbWe8f77/ggIaEyDqZeN1JBNQsA1CYgCxDXPu4xaCKQisGnTfoZqWUPPh0gu+qXaneaG559P5ILfTeYv+TLNerbaWaBAdly+rI/08+fBmKhM1pFGWbVqJ2Opxmm2DnriCTAETATjrjLRihQhIATskoAKQThlymAMHz4C77xTiEqGQOY9cscOrtufPQsoC7+ff1aL+p5o0ADM+QH89FM88yOFY9u2MGzfHkaPh1to124bw0N9yjwY3ZlcnXERLCgffzwLO3cupbIgnPmW0m9YmhGwVq4MpyfIOvTrN8FmSlalmOnRYwyVH7cZEpBPWxaUPFwTWLgwnDmUJuLatTsWtJSqQkAICAEhIASEgBDQn0ClSnUMuS/1kLx/vzcqV7bey1+PPogMxyagQozPmvUznzXqM3JEVuZfTX88ak2kffsghld/nsZTM9I17ExfotQQAs5DQBQgznMuZSRCwGoC8fEJXMCagC+/jLA62W+PHvFcDNxr8cKe1Z1O0fDJJ8szFn6KDRre7t3rz7Ay5dKUsGXLDjz7bGSadczd2bBhBHOBHDC3utQTAkIgkwg8+2xV/PnndIZtGs/rZE/msqhJD4Yy+PjjMvTkCuR1w82QRPK99xKhFBDuKe6wVB6MevVABXM4pk+/Se+xUWw3yyzFhPKQ+Pvv1fj220iDt4e5w+czE3NyRFEJsgPff7/G3GYW1VuyZBOty85DKcGtKUoJMmJEGAYMmGBNc2kjBISAEBACQkAICAHdCDRs2JIGJNl0kbdqlT+eeaaBLrJEiOsSyEJX7+++W4QXXpiI557LhcGDA2lcBUSkCLwRGgo+KwD/+18QjVLzoW/fmYZk9pmZo9V1z5iM3J4JpHg8t+duSt+EgBCwJYHfftvO2O3Rhljy1h5HhTUZMiScltLfWyvC6nZt2jRmDhLtSYajo0FlhAcXKtNOAHfw4ElUTLuK2WOpUCERBw9aZg1utnCpKASEgO4EKlYsSq+Kdpg9eww9Oj5hqIT7GD06guEA49LMfZHckZIlwTCDETh69He2mZm82ej/yMhoekhM5nU1wmLPPCVQXZcnTIjE55/PodVYiNFjaNk4ZcoP9IxJ8QRmhbBWrZLo3n8G589ft6K1NBECQkAICAEhIASEgD4EatWqzfuRQN6XaJOnDPOyZSvGqAKFtQmS1kLgXwIvvtgef/11BHXrfoX585uhRYv8qF07B1/BVI4UpDf6c/w/gxE9DtNQs7FwEwJCwAgBUYAYgSKbhICrEfjxxxVMxq1tEUsxq1ULOHHiLMLDozIUYfXqpXDnTlZDKBotB/7xR09aMj/DRUwG1E+jhIZGISgojQoW7FJynCF3igVDlqpCwGkIDBw4kdfOm2jZ0jIPCBVmcNq0SHqUrOHLtAfY4sUbKTsKKlyetSUbDRm7dYugwmaltSKMtjt27CK9P0JRqJDR3RZtVL8/P/yw2qI2UlkICAEhIASEgBAQAnoSUHkghw+fwMgIQfTStU5ybKwyCsxGD9cvrBMgrYSACQJefIB4/vkXMHXqIio6jjDU7hm+TmPjxoOYPHk+w9E2oQe6LPGawCebhQDk2yGTQAgIASoOLuri0aCsjVV8+gMHzmQoVXWzOmnSYIZRCWC8S+sOreJlzpwZyMTBr6UrwNfXG5H6RMAyyPH1ZRZjKULACQgoj4VLl27h6tU7SEhIcIIRmR7CX38d5DgPULlg3Tg9PWEIO6jCD5piNW/eMsqna5rG0qlTApO56xsGa+fO46hTR3vf1NDq1ElkTpX9GkcpzYWAEBACQkAICAEhoI1AkybNkC9fA3zxhWWJp9VRldLkvff80axZF1SqVElbR6S1EBACQkAI6EqAj99ShIAQcBQCanFx//4zBgVDeHg4/Pz8qLgoRqVDCSbk9rNqGNHRsYwrn2B17o/HD1qyZAQ+/HAGYmLCce9eJOW6o1SpAqhRowpjUjahJXOux5vo8rlmzTK82WxOJcjvXFSMgop/b265e1dZSPsblChBQQzWn04pX74Yjhy5iNy506loxu4jR9xQoUJZM2pKFSFgnwROn77KvBY/Y/36nQy5FIOcOd25oA9cv57I99nw0kst6CXRDFmzpv/dss8RGu/Vl1/Op4VfhCHMlPEa6W8tVgyoVi2SSdJ30qKrziMN4uLi6dkWgoIFH9ls1QflaebrG0d5oQgOzmqVjMcbHT9+gr89cY9vtupz8eLAuXM3rGorjYSAEBACQkAICAEhoCeBCROm47XX2mLUqH305IiEd9rBAQyH5qM57wsD+FzdDO+++6Ge3RFZQkAICAEhoAMB8QDRAaKIEAK2JnD8+CX07v0J83R04ULjh0x69S1DMM2nkuE7xoAcw1iQ3dC9+wcG5YilfVGLbJ6edN3QqXh7J1LhcYJJd69g69Z72LDhDpUSB3mMBWjXri86dx6Ba9fu6HS0R8WMHNkTefI8y8VWfy6+PrrP1Kc9e4AXXwxgUuL+aNToSVPVHtleu3YN5grRx2tj61Z/hg7TKaHII72UD0LAtgTCwiKZbG88unZ9G2XLrsbq1bfpjh2GpUvvY/ny+0zQF8akfZdw8+ZcPPXUa5gz53ezkn7bttf6SFdh686du4Dq1bXL69IlEj/+uDyVoLNnr6F4cf1u08qVS2TekQupjmPthmgmTfKx3DjS6OEeeOtbGWvCqETZKASEgBAQAkJACAgB6wh4U+Mxd+4yGo/0RPPm2fDHHzAY9xiTpkJeLV3qRkO87KhSZSgmTpwhYYiMgZJtQkAICIFMJiAeIJl8AuTwQiAtAiosyuef/8AE37/SnTYc48fjMc8GtWAUjkSGn9+8eSfefPMok141orVKL1qqMMi8GcXPLwsVKvotPN2hbqN2bdDK+MHBlcWMShhesWIievYMw6pVe3mD+CY+/fQdtG79qMWzGd1Ns4oKhfXZZ/24+FoVbdt+TYvqKC7OxiB//kebKffkgwfBmPj+zFmSnUqk9+mFUeTRSml8evHFpzF27LdMThxjlkWQKVHK8+TQIe90k66bai/bhUBmETh37joVmoPpOXWP3+V4PugZ70mBAqAVXCx69YplTOVZWLduGx8oR5l9fTIuNfO3Kk+8atWSNHl/JI9CRUg4ciS1YiIqKpYP3vpdm/38khAVFZN8WM3/szG5yL17msUYBFCXwiTvckuqD02RIgSEgBAQAkJACGgloPItDB06homlX8Y334zFyJFbqOBwQ/nyocyBFo/79735HBfAezjQiK4ljVkGMy+aDonRtHZc2gsBISAEhIBRAvK0aRSLbBQCmU9AKT9ee20M/P0PYM2atC1t1eJjw4agJ0g4FQBr0KHDafz001i64Kbvr+vBWFFZswZwISsM2bNrH/cB5vN9+WXjclSOkFatkujJEso6E2lJM4A3lXWNV9aw9cUX6zIJWDXeiK5Djx7LmWQ8jJbUblxMhGHB7sKFJJQs+QTeeKMTrXqqU6lkQbws9svf34deI8/yZng1+ven2Y+VZdw4XyqtXhIrISv5SbPMIXDjxj20aTOIytk7BmWnOb1QibinTo3EhAmHeF0bjQULPqLygBcEBy0nT17mNUSfREAqF4iXVwJiY+MeUQxlzerHh2v9GN2/785rvXWhEo2dpsqVy9ETbhUVYVHGdlu07ehRcEGhiEVtpLIQEAJCQAgIASEgBGxNoEyZsgyvPJ9GJFFUdhxh7syjhve5cgUwWXollCtXjvdxXsxDSYtEKUJACAgBIWC3BEQBYrenRjrm6gTef/8bxs8/gNGjo822Mua9F95/PwaTJ58yhKaZMeN9szA2aFCD4Wt+Y3gqbTdu9++DCZCBIkXSPmwupgFZvDicHhpfMoZ8KVrL6JBM47FDBgT4UsHxvOGlFhbPnLkGle8kW7YAFC6cW7PSYcSIHlQ4baXi6RaT3D12cDM+btjgRu+TvPj66+fNqC1VhIB9EEii+9Rbb42lV8c9s5UfyT1X+o7Bg2PQp89RKkDWokuX5sm7HO6/Ch2owv3pVdS1Oy4u4REFSJEieXD+fKIhoaYeuiKlZKhQoaheXWbi8nL45BNPXfq3bl0WPPNMLd36JoKEgBAQAkJACAgBIaAnAV9a0lVn7FP1Si4q16Uy6FH3x1KEgBAQAkLAvgmYCFph352W3gkBZyewbdtRWtb+yVBW5is/UjJRXglXruxjYt3tKTebfN+rV1vG5/c3LGSZrGTGjh9+ABMew2Q4nJQiVIisMWPC0a/fuJSbbfJehQMrW7aQIVl80aJ5NSs/VCd9fbMwz8nHOoYzjwAAQABJREFUXMwNMoTTsqTjmzeDieJzMPTWR7r0xZJjS10hoIXAH3/s4XXiDD2grFv8Vwv548ZF0lNtNkPvMe6Rg5bg4CDmNtEnD5BCoBJn+vo+6rHnTte+4sXzG0IraMV09aryMglgyAa6welU8uTJjmLFimPbNm0CVezsZcu80alTY22CpHW6BFTummnTfuH3dwA9bjoyNGUHKsU6cDGnC8PUjWaIuj0MqWnddzvdg0sFISAEhIAQEAJCQAgIASEgBIRAJhEQBUgmgZfDOgeBmzdDsHbtbi6Er2e4pY3YseOYLjHWP/jgK4aKCX8s34f5zNQi48SJEfQe+cYsixSlFKhatRrmzrXeKezyZZADGHLK/H42apTEhb9LdCW+aH4jO6pZrlxhLFz4GZU4uTBlijctuNPuXGSkUnz4MH/IE1i5cjIKFMiZdgPZKwTsjMDUqQvwzjsRmnqlQu09/3wMvzvrNMnJzMZVqhRn3Gd9FCC3bwM5cmQ1qgzt1asjZs3SHrZq/vwsDD3WRndkw4a9jg8+CEj32pfWgb/4IgsV500RGKh9nGkdx5X3xccn8J7iezz9dHfcujWXccyPY+vW+1RehWH79jAaS9xC+/bb+Bs+FrVqdcc//xx2ZVwydiEgBFIQiI4uy0+yZJACibwVAkJACAgBISAEHJCA3M044EmTLmcugZiYWMyc+RvDv3Rn7PM3uJA9lmFKJuPkycn46qsPmN/iFS7mDMHffx+yqqPHj1+iBeZtWmVa1fxho4IFwdBSkVzcOPZwW1pvxo3rx6TgQdi3L61axvcxJCo9IZRHBxAUZLyOqa1du4bTE+JXU7vtfruKW79ly0xaszdnMvNAhoTJwoT0gFrUVFxu3ADWrwcXnHwYLisr8uRpjz///A5PPME4YFKEgAMRCAuLxOXLV5gAUnunO3WKwc8/r9YuKJMklCiRHxcueDC/kPYOrFrlgWefrWlUUKtWtXD4cDao3ErWlvPnwd8pf4Y4bGqtCJPtlCKoZctmBsWuNdEftmwBNmwIxnvvdTd5DNmhjcDt2/eZE6svf4uWYtOmMAwaFIvSpfGIgYW/v8ohBnqHhGPGjJsYMeJDGlDMMMuAQlvvpLUQEAL2SsDDI5z3ttWZry+Q1wIV4sdeeyr9EgJCQAgIASEgBIRA+gRoJy5FCAgBUwRiY9c8cru/Z89JJq3+BPXrh6BnzxguYqduqR4QduwAPv7Yk8qA0gwtNcpg3Zu6JpAybmhy4rRp05YjMnI6FQraw1D89BNw9epLzAvSw9jhU207ceISE6gP5iJ+iCGpeqoKRjaoBf7XXwdDaljm/ZEs6vp1ME9HIWzcOD15k8P+Dw+Pwi+//E2l026GrTnL8xhLq2Yf5ggpScvbmrR6r2NWYnpLABibQ5a0l7quTcCS+aOUunPnjmEiyDDN0NR1slatQH5PfnLYZOjjxs2j18YS9O0bbzUPFW2oSZNAKoOmoWBB40rRAwfO8vdmCFasCLdYwayUsO3b+9NLYwQaNXrS6n6m1dDNLQldu37I/EoHqAiJZqittGr/t++PP9yoNM9h8IYzNfb/ass7awiokFfNmvVD//7X0bq1+fcU8ZzSb73lhxIlmlMR8oY1hza7jSXXILOFSkWXISDzx3anWilAQkMbUPHh4dTKD5lDtptDriBZ5o8rnGXbjtHV5lBwcHZZg7btlBLpaRAQD5A04MguIZCSwMqVW9G793Amrb7B3BzGlR+qvgo/Vbu2sriNR9OmR7j4/TI9APamFJXm+337DqByZfMXKtISVrkysHev+aEsSpcuyH5/yTGWYJgbX1o4m5auwjnNnQu88MID7w9LQl+llJo3LxhLPyTlJod9r+Lrd+3alFa0w+kFMhO7ds03KHYmTx5MxVJ93ZUfDgtKOu6QBM6du4aiRbmirkNR18nsvP+9f19bOC0dumK1iD592jGZewDzLVktgt6EXvQarGFS+aEkV65cjMnj+9Cz0B9KYWxuCeFl9dVX/Xnt6WQz5Yfqi4eHB0MnjaaivxVatAigAhhpLpYpXgMG+NHToCRWrZqS5tjNHavUM05g0KBJ9Py5aZHyQ0nyZDTMqVMj6d24lr9hVriFGu+ObBUCQkAICAEhIASEgBAQAkJACGQKAesD/mdKd+WgQiBzCOzefYJWkF/QSjccOc1M26AW+Lp1A71EEtC9+wgMHNiFr87pDuD27RDkMm4InG7bxyvkzg3cuXP/8c1pfi5cOA9Wr57C+PwbqPBZBB+fMCZIjUXJktGGRZHr1z24wOXB+PexDAEGxg4HVEJzLUWxkiIE7IWACnO3Z88pvo5zwfk6lZruTPZcBNWqlaZCs6jDeixo5au8Ntzd+UenwnVzhvvTT55O3TJbTLZsAUzo/g699cZj8eII+FmYwmL3blCBEoS//uqX7jE7dmxMb7IAtGkzCf/7XziVIQkmPS0SEoDffnNj3wIwfPgbePnlJunK11pBKUE++qg3vdwa4NNPpzOE0nk0aBDH70s0FSNANPPdHzvmgZ07/XHtmi/H8DKVM81c9ruklbc57bdsOYyLF/fhiy+s81BSSpAvv4xAly6fG5T5np78wkoRAkJACAgBISAEhIAQEAJCQAg4IAFRgDjgSZMuZyyB6OhYLnB9YoiNba7yI2UPGzVSIaKS6C3xo8FStn//jil3p3qvFhnSS6adqpGJDUqONYsW7u7ueOWVJobX2bPXDIvBZ86cZxzgBCqAcqJIkQsMA/YbevUycWALNqsQMImJogGxAJlUtRGBO3dCMXHi9ww1tJlKvyTmuojgAm4i5ydw9qwnlaB+OHfOi+GI2lA5+CIXoF3rJzRPnuzYvVsl/qb7lw7l7t0kZM1qodZAh+PqKaJly1pcZH4FnTr9gOnTI6A82swp69a5MVxUNixdOh7Kc8yc0rJlbYYNm0FFw3TmG9rB0FnxnKNR9MpRiimwH8D+/T5Yu1Z5lVTCmjVvokABMzX25nTAjDpVq5bEkiUTqOS4wyTbR5lT6jBC6Iri45MFZcqU5neorEsrEc1AqFuVKVPm03MowuCVaq3QIkXAuRRFhdp2hrl82lox0k4ICAEhIASEgBAQAkJACAgBIZCpBFxr9SZTUcvBq1Wr5hUaGjrSzc1tOGkoU8LRJ0+e/DAtMuXLlw+Ii4v7H+swwwSYthNqtewmX1sSExO/PH36NINt2LbMnbuKCUTvo1w564/Tvz+wbFk8k30vZqLRqrQkL2VSWMmSRXHq1GGULWuyitk7Tp4EPTcKmV3fWMVixfLR+j0fdz3zcPeGDXvx00+b+Fl7+Jpz58AFvDwPZcsbIZAZBH77bRut5b9kPppQJqlPhG+qNWllRR1qSG4/bdr3eOaZVQxf9AHKly+SGd3NlGNWrVqC+YH0uW24fRvMGRFolYI2UwafxkH79GmL/Pnz0DtjCpXdYejYMREqqbSxoq53X3zhx4TUT9B7biQ9BC1z9wsOzorJk9+Fyje0YcM+hjg8xMTWlxlyKonKjnxUkFTCsGFVGF4s0NjhM2xbvnzB9BCsZ3hl2EHlQA8JqPlx8uRZzoeHm6x+8+qrkfjqq+WiALGaoDQUAkJACAgBISAEhIAQEAJCILMJ6LOSkdmjkOPbPYFSpUqVCQsLW0DlRzVzO1usWLFCVH5sYP0S/7ZR0c/Vqwhfneil0Kl06dLvnThx4jN+tklRi0qzZ/+CH36I0SRfhZJ47TVwQSKSoT/G0jJ2tsnQHzVqVGZIlPUMJaI91v7OnV5UtlTR1HdjjatXL81wXu4Gy3hleaylbNjgycXkGlpEWNQ2Pj7BENN827Z9OHz4BBOVR9MC249WyWWpnHqSltUVaU2tcVAW9UgqZzaB2bNXUjk5lwns07fgV15gH3wQQ0v7q4ytP4R5DMbQQrpMZg8hQ46vPEASEvxx+XIoF+61HXLFCg80b15XmxA7av3880/j6acrUjmxkPk2NqJChSReU9R8SoBKKH3qlA896bz5PiuTS3dmCKuGJn8DzBmW8hp54YWnDC9z6ksd1yKwf/8ZPPmkmybvj2RiFSuq8GWXkj/KfyEgBISAEBACQkAICAEhIASEgMMRkFU+hztlDtdht5IlS6oA5yoLuFJ+rDZzBO6enp5LWVcpPy7wVY/eIvn4Kuvj45OTionxSg7/j6NypYV6b4ty9eodLo5HI59ygNBYnn8eOHAAKFw4jAqOgyalNWtWHX/84YkYbToXg3Lil198aJVcz+SxrN0RFORPxUp5WiBbK+FBOxVa6IcfVOLwVtoEmdFahe+aMuUnVK36Cr1XxvI8LKW19mEmCD7NcEcHadm/mPHOR3PR6FWGK/vdYFFthlip4uAE/vzzAJWcc7FoUfrKj5RDrUK94vffhzIc1ijmCbmbcpdTv+/Vqz1DPakwWNYXFZpv3jxfsnvBeiF22FJ5Z6g8GPv2/YAhQz5B7tx96enxMkNAvYo6dd7FnDlf8do/kx4iz2pSftjh0KVL/xK4fPkW85/M5u/jm7xveIkKsA6G/+XL98Sbb35Jpfv5DGF16tRlen/qE6pOGXBkyZLAPC6xGdJ3OYgQEAJCQAgIASEgBISAEBACQkBvAnyskSIEbEeAyo9W9PqYwiPE8P8AemtMocKCy95pF3p2vEzlRnXWSqI1frvjx4/vSW5x8OBBFXdpKOWokFhqBW0iX+YqVljV/LJv32nGWOdqnQ5FJTa/dw+MyR3ORf8VzKFR2ahUPz8fWvXW52LZWuYesf7YS5d60DK9MnN2BBk9jtaNgwf3YHLUw/SYCGd8d+ukzZrlRavpmlwkYpZcG5ZLl24x4e4IKj9uUvkRzRwPKrkrDGFqlHVrEBEpK+3Q0EjmX4nEd999w8XtVUxs/KnN+NlwuCLaTAIREdEYMOAznusIKjrNbJSiWrFi4GJnGPr2HUvvkQkp9jjv21dfbUqPrZ9x8OA1ejhYN84pU7zRosWzXBgOtk6AnbdSuWGefLKk4WXnXZXu6UQgJCScv9eTGY7sgOF3RCncUxaVX+jUqSsMhbmFiokCWLBgGJXweVJW0fV9XFwCvL3TvdUy+5heXm78bYznb7232W2kohAQAkJACAgBISAEhIAQEAJCwF4IiAeIvZwJJ+0HlR5KyXaU/2tS+cElZySZM1QqPxgwylA2pVR+PNb2838/l6WipdZj+3T5eOPGPeTJo9EVI0VP1EK7yu2xd++pFFtTvx0+vCcXSAJx+nTqfeZsuXZNxZkPwKef9jOnulV1ypYtRO+J5/H++770lrBcxKFDygo8K/uoUrzYrijlx3PPDaDi6SKeeiqafVZJ12FQgqxZA0zguvX77wNjxwLLl4PhsUBlSTzD/Jxh/V70DLlvu86J5EwlMG/eKs6NcEMSaWs70rp1Ii2jz6f7nbZWvr218/b2wrffvs9QfoH0fLG8dyr595o1ORlGrJfljaWFELBDAircVKVKr2Plyu00cghjmLhHlR8pu3z3bhh27DjO3Bz/4+/NPyl36fo+R45A3LqlzVMrZYfCwpLg56efvJSy5b0QEAJCQAgIASEgBISAEBACQsDWBEQBYmvCIn9XZGRkdSo/TMd8eoxRgwYNlNKk7r+b1z22++HH/Pnzb+eHcLWBCpZnH+7Q8Y2HhzsXM9x0k6gW3gMDwQXT6DRlqvju06d/wLwhgbhkYejtm0wR/8orAZg48V2bey8MHvwqYmOrMumuL61D0xzSIzt37AB69w7Cjz9+Sst730f2pfygFpKU1axSGB09eoHHsuAgFKTyfXTuPJxKljsMSwMmrVbJ6IG33wZymHA6UcmLu3UDLXkZs61aGK3de6W5oJWyv/LesQjMmbMc3btrD+vSq1cYv68/OdbgNfS2SpXiGDduCJWJgcyFYp4gpSSdN8+T38Xc/A5+zpA6YkluHjmpZc8E1O9SixbDqAy8Z/CQMLevShHyxhtfUGmyzdwmFtWrUqUEvbT0UVjcZYS/oKAAeHh4WNQHqSwEhIAQEAJCQAgIASEgBISAELAXAqIAsZcz4aT9YM6OK5cvX46yZHisX5r1DU/uVGwcNdX2zz//jOe+0//u1z/TNwUXLZoXZ8+aXqA31Tdj29UCYGgoDKF23NyS0s0xoRIrT5nyAZPlBuHXX837qm7Y4IYXXwzEqFGD0LSp7ROLq2ThM2e+j1y5WjKhcQAtW1VeFmOjf7BNhQD78EMf9u8JLF36BcqVK5yqsgqzsXTpX1xUeotWtZ2YbP0derOMoJfGEObq6GxQSMyc+RtU+KL0yuTJi2hpewvnzgG7d4N5HmB2PpesWcFFbWri6oahffv30juU7HcwArduhTCcSzQ9vLR3vEEDYNu2w9oFOZAEdX2ZN+8z5rrIj+HDfXD+vPHOq+vB338D7doFUFlSG+vWTbV5yDvjPZGtQkBfAkoh36bNKKjwVtaUkJAIKkEm2SSHUPHi+ejF6I77OjgwrlrljoYNa1ozRGkjBISAEBACQkAICAEhIASEgBCwCwKSA8QuToN0IiUBJj9/IlG5SrDQA+Byyn1G3qv9SvnxhJF9mjdVrVqCC/D6eICcYtSrIkUedCkx0c2sJLj16lXkguE39FgYjxkzztBaPRwNGiQhOEXo/JAQYMsWMHF3ILy8CtCi1LaxxR+HqpQgo0a9juefb8AEwN8yJ8JFNG4cg8qVY+mBAnptACdOuGHnzgAcOuTFxMcv0nq8HTw9U1uT7tlzkoliPzF4XowZE2kIF/b48S5fDmPOhhlMKryAYasGoFWr2o9XMXxWi1Nz5qzgK5rJzoG1a8GY6EarmtzoxlOvQmQ1bHjYkLj+mWesTHpg8giyI7MIPAhbYzpUjSX9UvPKxyeeCs4IZM1KFyIXKZUqFcPmzdOpWNzIPCg/ISYmlN/7RCqV4njtBs6cycKkz4nMvVMao0e/wu9sORchI8N0BQLjxi0yeH5oGeu9e+H0hpzEkIwfaRGTqi2NRxjKsSV/KxczXJ2yFbGuqFuxOXP8mTurrXUCpJUQEAJCQAgIASEgBISAEBACQsAOCIgCxA5OgnThUQLM/8EgUQ8KF9cjk9+b+J+8/2EbE/Ws2qziaBcoUICWy8eZDN0qEQ8bLVwIWouCVplAwYK5qABQXh3JyhW3fz8/rP7wzRNP5GSC5fFUIlyiEuQXzJq132Bx6uGRxFwWbobQFLVqVaCXRDt6TBR72C6j39SoUZqeKpM4vltcFD1Ij4uD7OddKh28qcgozUWYcsypUQ5KYWKsfP/9Wkya9B2++ioc5csbq/Fg2xNUdQ0bFoOuXWOoGJqAPXuac3H1jVQKpV9/3YVnn43D5MnK6+SB541pqab3qKgfs2YloVev8Thw4EfTFTNtT/pzKNO6ZscHVsqK4GDrFwYfH1qOHO4ID49iaDWbXIoeP5yOn7XNH09Pb4bqa254qUTQ+/adhsqdpMLldOqUDxUqFOE1wEvH/ooo+yOgbQ7Z33jS75EKrzh9+u+IitKWI0wZe6icIDdv3kP+/CksG9LvQro13nqrPX9zV9Er9B7U76Y1Zc4cT9SoUR3Ko8R2xfXmj+1YuqJkmT+2O+sP2CpjIBUS2FmLUhirov4/eDZz1pHKuGxBQOaPLai6lkyZQ651vmW0mUtAFCCZy1+OboQAFSApY06lF6A/efXBz4gozZvUD1L//l25MP8Jw71EWC3v9m1g1SowXAxokemJRo2eemTB/sG9d/JDnPHDlClTiHk9BjzcSU6PyHi4I5PfFCyYm5anjQ0vc7uyYsUWfPPNd0wKG04LevNaUS+FxYsj0KfPanLxZ5LzLo803LJlF5VWUfj6a9Aj5ZFdFn8oWRJcLA+lUuckF4NUhDb7K+bMIfvrdeb1yNvbkx4L+j3Qx/BKlCWLl11+J82hrMf8yZ49kErHquYcTuo4IQE95pCjYPnrr4MW5fxIa1yhoZHMh7WRv2EvpVXN4n3ZsqlcYIP4GzmWHhwRTGJumYi9e1XenmzYunVAhlzXXGn+WHYmpLY5BGT+mEPJ+jrJC3TWS3CMlq4yTsc4G47XS5k/jnfO7K3HMofs7YxIf5yNgChAnO2MOsF4uLAfmeLin17QIp9/h5zsCaIrAaVkaNq0Gr77riS9MPajrRVRIFQM/MGDgaFDH4RgWrDAl2Gtnvs3B4gKhZWcN4MVLSyqf45elOXr0KEqBIj5yo/kMXvyCjZ1aiRatvwFTZrUYfid4sm7mAD2hCG/Q8uWyqrr4War37z8cizDi21mHpJSVsuwTUNtc8g2fbJ/qaVLF6Rnj7q8WJSiyOjA1Nfw+vVE5M6dLd3cPkYFZOpGmT+Zit8pDu56c+ivvw5B5fDQoyhvkvXr9+LddzvoIe4RGc2b12R+nq70xprP+5gIs3NgbdgAhrbMbvA+DQjwtfF1zfXmzyMnST5oJCDzRyNAs5o7w/OGqYGmeOa08bXOVA9kuyMTkPnjyGfPPvouc8g+zoP0wjUIiALENc6zQ42SN9lhyT8EDJeUXkD9gH8HZ10W0nTIxMc/yEXy9dfvcYH9TS6o38PTT6fTKMVutTD68ccwWF6q8Fdff+1N74+69CYIgpL9XxisJMPnFE1d5u0HH3yLd94J5+KxdUPOkgX4/PNwDBgw3pAvJVlKeHg0cxCAFunJW7T9VyHQli8/aHfnSeaQdee1ePH8DCuXyPMJfg+tk5HcSs2zwoVzM++FUkg6llJS5k/yWZT/1hJwxTm0Z88pXRfKTpy4bLPfll69XuC9Sy60azeFoRxD0bFjIvxN3FmdOwd6vPrh2rUnqPAfaQjXmXwfZO38SK+dK86f9JjIfvMJyPwxn5WlNVWoXVXUs4ytrwOW9k3P+moOqedOpeRx5nHqyUxk/UdA5s9/LOSddQRkDlnHTVoJAWsIaFz2seaQ0kYIpE2AseMvJFsa8b+KXL0njRZF/t13No06mncpy+4VKybR0+BthnYKZT4LMJdF2mLv3HkQ8sqHPipffgnG+QaWLs2Gv//uk3ZDF9qr8jBs3rybSqIHiiZrh16VUXeSkm7jyJHzzB9SxCDG3z8LF3HAHC7WSn20nYqhfu3avUc3yieHJaByVDRqVB1r1mxC69bahvH997545ZUXtQmR1kJACDgMgdhYak51LHFx+sp7vGvPPfcUjTcqMifWjzQK2MB8YeArAnnzJhiUwKdP+zDEozfDemVF374vU0nybIaEvXq8n/JZCAgB+yDg6RmK+/cb20dnpBdCQAgIASEgBISAENCBgChAdIAoIvQlcOLEiVOlS5eOpPLDjwlCK1D6CmNHyJ8/v4pobYh5RMsdRqu2bSlSJC927ZqP559/Bz/8cA79+oHv8UjOCmUldf48GM8bTAj+QAGiwmatW+fGRN3ZsWzZZ/D1pcuCFAOB9ev3McRYnGYLfCWsQ4dwKqk2P1SAVKhQEocOnWOieH1gKznu7jrE0tKnO3Yr5dixi8xzsxI7dx40KIyUojApyQ0lSuRDvXo1mby+JXLlymYX/e/btxOTd+9Cs2bh8LIyT/eVK+r77YNPPqlvF2OSTggBIWB7AnnyZNf1ICp/jq1LjhyBGDOmN0aO7Gn4bdyz5yRD991mcmMP1KxZGG++WRLqPkeKEBACrk3AwyPcoPxQ927quUaKEBACQkAICAEhIAScgYAoQJzhLDrfGKj3SFxPpcbzfLXg8D4xNkR/f38V3MiQI4T1Vhmro/c2f38fbNjwDT06lmD8+Pn46qs4Lh6oBNnKPRxQXh/K40ApPTZtAm7eBBUlfrh1qyCToI/kvpx6d8mh5e3ZcxBPPhmtyxiqVk3CZ58dfCirTp3qVFhtwNmzCcwN8nCz1W/O0seoSJE8Vrd39oYXL97kXB+H8PBL6NIlHN26JSF//gf5VxISgNOn7/O7c5oKr2X0vHiaC3F9GBouOYVP5tApV64w+9OEIdRWYdgwZjG3sKhxDRjgj3HjBsDHx3ApslCCVBcCQsARCdSvXwlLlmxGTEycLt2vVauMLnLMEeLl5cnf3ZKGlzn1pY4QEAKuRcDNTSfLIdfCJqMVAkIggwlERETwuTOcxqU+NEgNyuCjy+GEgBBwRALpBPFxxCFJn52BAHN/zPp3HE/TG8RY1g3qPNyG/Fvn7+PHj5/MyHH3798BW7bMRYsWzRn3P5Dxtf1pRe7GRVQwFA5w6VIWdO6sQkk8wTr/w2+/TRblh5ETdOnSFSoVjOywYpOSc+nSrYctW7eujQsXvBnW4+EmTW/27HFDtWo6aFI09cI+G69YsQXPPfcWevY8Ri+cMLRvn2RQBDKksqEoJWHp0uD3IR5//hnGfC8bULfu6zh58nKmD2jkyB7Ys6cQZs+2zAXkgfLDl8q1BgyNVyvTxyEdEAJCIOMINGhQmXk09FHgBgX544UXnsq4zsuRhIAQEAJCQAgIASHggAT27t1L47NuDOtZhiGMK+GNN+ryufNJ1KpVgsZ3rbB27RquzdBCTYoQEAJCwAgBUYAYgSKbMp8Aw2AxgBQ2qp4wFNZPJUqUeBhfpkiRItlKlSo1g7vq8ZVAb5EBql5Gl/z5g2k5PoAL7Avw9ttjkC3b60y63Q5XrnRElSoDMX36l/jrr5kSSzuNE5Oc6yWNKmbvUovtKeUpi/yePVsxkSsY19xsMSYrLlkSgDZtGprc76o7Vq7cirFjJzHkWxhjy6cfK0EpQ/r2jWMs+lu8YX2XHjpM1JKJxdvbC7/8MoGKmTJ46y0/hISk35nTp8EFywAUKtScY2dCIClCQAi4FIHChfOgZMkCuoxZ/VY1bVpNF1kiRAgIASEgBISAEBACzkbgypXLXFNpigkT2lHx8Ssjbdzgs9tdPn/eZijiu9i27Q7eeWcrVq/ujcaNq3J9RicLSGcDKeMRAi5O4F/7XBenIMO3GQEqKlRoKgbCeaQkm9Hf4NbrKffQq6MllR9X1Ta2VfGi1vFVRX1mUSuloXwV5UvFm4njgne3U6dOLeR7m5TY2DXpr+hqOLKnp7sh0ahauI+Pdz2X8/fem0Jl0SrNSajVKThxAvjoozJYvnzywzOiEsuWL9+BN0RRDMv0cLPFb7ZtU4nsS9OTh9ns7axk5hy6cuU2mjfvSyVTKHJaEd3tn39ABUJhhsaaZohDn5lo1Xdw/vw1DG03lzfOsVRwRKECMxD5qUxDLPfugZ4iwOLFAVR0+mPSpMG0PmIFBy+ZOX8cHJ10/18CrjqH9u49xevfMCpNw62eC8r7Y8KEN9C9ezOrZTh6Q1edP45+3uyl/zJ/9D8TyQnQXSUHiMwh/eeQK0mU+WP7s/33339h6NDXGD75Hpo0SX9p5tgx0Dg1iIZ279BDpL/tO6jxCK42h4KDs8satMY5I82tJyAeINazk5bmESjHakrhkfKV3FIlVEi5vTIVIA8D6Z88efK2l5dXLS5Mql+urXz58lWELxU3ZwbDZFWwpfKDx5BiYwLVqlXBvn36hBHZvx8MUVXpkR6rWOeLF0/Axx+74apBrfbIbrM+REaCN10BmDjxXbPqu1KlgQM/xwcfhFml/FCcnn6aF4DKN+kttSLTsfHaQ9fpFpyPC/DUU/2YyL0mFzeD+T4IdeoE0eooH9avb4A+fUZix465TqH8yHTo0gEh4MAEVB6Nrl2bICDAut8wb29PXv+K8brT1IEpSNeFgBAQAkJACAgBIWAbAsqTY/jwbsy7dtcs5YfqRdmyoGfIfT63fYF5876zTcdEqhAQAg5JQLRvDnnapNMZRUA8QGxL+u7dMDRs2AN//x0Gd43q2I4dA6no+BRVq5ZM1en58/9ggvRJdItNQo4cqXab3BDN/OyvveZHD5Uu6N27jcl6mbkjs6xGLly4gU6d/oc//gijF5P1BG7fBkOLBdO74nvOAY2TwPpuuGzLzJo/LgvcCQfuynOIITjx0ksfYePG/UzEGWX22VXKj1KlnmCYzElUoCjbDtctrjx/XPes6zdymT/6sUyWJB4gySTkvxBIn4Bcg9JnZG0NleS8efMamDPnGsOOWi6Fzdk+G2bOXKsii1guIINauNocEg+QDJpYchijBGS1ySgW2SgEnJtAbGwcIiKioRZvMrPkyBGIGjUqMrSUhhV0DkC5uoaFZTOq/FDj69q1KT78cAgT1Xtj1y7zRqzyPLRt68+8Fh3sVvlh3khsU2vx4j/QuXOEJuWH6pkKnVWyZCzPC2OYSRECQkAIOBABpbT96aeR6N+/DbJnDzCr59myBaBdu2eo+J/s8soPs4BJJSEgBISAEBACQsDlCHz11Vh64N+1SvmhYPn7g2FGQ+hB0sfl2MmAhYAQME7A0/hm2SoEhIAzEYiPT8Dvv+/AwoUrcOTIBQ4tlgvXCbRYTWSOAx9UrFgW/fp1ZLif8hk+7NGj+6JFi8N45plQJpK3/PDx8WCOjwB88cWgNBt36NCQ+UCKMRboR3jiidtUakQzZBYe8TxhGghDLpG5c33xzz9++OqrYahb1/HzPKQJxsqdO3fux5Ah+ijQatWKoALkGGrVos+yFCEgBISAAxFQSpBRo7rSE6QB/8+jV8dBGhck4f79CKjcQqoEBfkZ8hyVLVuIuapek98VBzq/0lUhIASEgBAQAkIgYwnExsZi2bJFTHQeo+nAtWtz1SP2PBha3a69QDQNUhoLASFgNgFRgJiNSioKAccksGnTfgwcOAHVq0fhzTcjUYlpMry8/hvLrVtxVI7sRM+eO5GQ4IcRI96gx0QzQ3L2/2rZ7l2BAjlpmfE6evT4Fj/8EAFfC6KBKAeWwYN90ahRU9SsWSbdTpYrV5iLU9OZdHsfvvlmMZVB55E7t1qccqNHDHDlSiKKFMnLvrRnkuunyUkukaagnjp1jTeSpvZatr1MmUSsWXPSskZSWwgIASFgRwSUckN5g4SFRUIlSFev27dDERjoiypVSlDhXhK5clmh5bejMUpXhIAQEAJCQAgIASFgawL//PMPDTPjaaip/UgdO4ZgxYpFXDP4QLswkSAEhIBDE5DVPYc+fdJ5IWCagLI8HT16JsNsrGVC53Au7BuvmysX0L27ChPFzPIzImnB+iUThv2KBQs+Qv78wcYb6by1U6cmuHs3lGFBfsTUqREoWjT9A9y5Awwa5EdvjqcZ3ur19Bv8W0NZ6zZpUs3wUoxu3LjHBasoKl68oZQxKhm2lPQJKAtnT51+QbJkUdY5cekfVGoIASEgBOycQGCgH+rXr2x42XlXpXtCQAi4MIF798Jw4MBZhISEQ+UlKlOmEO+/87owERm6EBAC9kJg//7tNN68r0t3qldPwpgxW3SRJUKEgBBwbAI6LV85NgTpvRBwRgIffTQLp06txtKlkWYtVKv80717A08+mYQ+fc6iceO36BkyOcMehvr2bYeyZYujS5fxTFgWQaVMDJUbqc/MvXugp4gnX34Gz5FOnRpbrbRQyo68eXPwlfo4siVtAoGBPgzxorxn0q5nzl6VCJ0J0cypKnWEgBAQAkJACAgBISAErCCg8v8tWLCWSYV/4b1zFL3CExl+NoFGKG4M++qFixeT0KpVDXTrVgKFC5thjWRFH6SJEBACQiA9AhcuHKUHSHq1zNtfogRw/vwl8ypLLSEgBJyagChAnPr0yuBclcCWLYexceMaLF9unvIjJacaNYBx44CJE0PQvv1gbNkyk94RNNHPgNKwYRVs3TobixZtwOuv/4LIyFAqRdy4yJ7I9244etSNYbq88fLLLRkTtDUXzbNmQK/kEMYIVK5cgpaD15i7xdhey7YdOJCFCewrWtZIagsBISAEhIAQEAJCQAiYRWDz5gPo3388FRxh9AyPRb58qZvFMNz+ypUbaYz0LNq2fQNvvTWYRlSyXJCalGwRAkLAlgQSEhIeydOp5VgeHmBuNsbNliIEhIDLE5A7GpefAgLA2QioH/gBA8Zj1qxwszw/jI2/SRNQeQLEx99lSKzvMH7828aq2WSbSsreo0crwysyMtqQtF3FVFdKGBVjPVu2AJscV4RaRqBhw6ewdu1uKkCiLGv4WG2VI3jjRm/06yfJ5h9DIx+FgBAQAkJACAgBIaCZwOzZK/lcMBfz50egWDHT4lRI0vbtk9C6dQg++mgqPUG2YPbsX5BF7ZAiBISAEMggArlzF8T16/oc7No1MOdnxoT11qfHIkUICAFbEWDQGylCQAg4EwGV4LtcuQgUL65tVAMHgq7wCVi//i9Dngxt0qxrrZQhNWqUxrPPVkWdOuVE+WEdRpu0evHFukwm7838KdrE79gBFCpUJMPyzWjrrbQWAkJACAgBISAEhIDjEPj113+o+JjLkLhpKz9SjsjHB/jkkwjeg+/F2293S7lL3gsBISAEbE6gcuWnsG+fvy7H2b8fDPdXXRdZIkQICAHHJiAKEMc+f9J7IZCKwKJFK9G5c0Sq7ZZuUAoU5TLaunUU5s5daWlzqe/kBLy9vdC370u0EPS1eqSxscCIEQF89bFahjQUAkJACAgBISAEhIAQSE3g9u37vMeaQu+PCARY4UA9YEA0oqO34ddff0ktXLYIASEgBGxEoG7deli3zpuhq7QfYMWKIDRq1Fa7IAeSkMQQC7t27cLQoUNQv349GseWMbwaNKiP994bij179jjQaKSrQkA/AqIA0Y+lSBICdkFg//7TTGSuT1eq01gid+4EbNq0XR+BIsWpCPTp0wZnzhTAihXUlFlYVOirkSN9qGBrjipVNLorWXhsqS4EhIAQEAJCQAgIAWcn8Nlnc2msEo48eawbqZsb8PnnoRg7dhhz8CVYJ0RaCQEhIAQsJJAjRw5UrFgLf/xhYcPHqqvwV0eO+DFksw5JKx+Tba8ft2/fRu+9asyZ2hEzZ87A4cOHGc3jhuF16NBBzJgxHR07dkDNmtWpJNlpr8OQfgkBmxAQBYhNsIpQIZB5BGKYwdDPT5/jlywJ3L+vQmHd0kegSHEqAu7u7li4cCy+/joPFizwgFJqmFOio4HBg31pVViNlok9zGkidYSAEBACQkAICAEhIATMJBAVFYNVq7bgpZe0KS5y5wbD0EZzIXKtmUeWakJACAgB7QSGDBmLMWOyWR1uWT2XDhwYhGHDPmNCdedf9lR5YJV3R+fOL+PcuXO4d++eyZOg9p05c4aKkJdokPg+n+HNfIg3KVF2CAHHIOD8VwLHOA/SSyGgGwFlraVX8fSEwfXUwyNJfhj1gupkcnLkCMTq1VOwdWs1vPKKP44eNT1A5ca8fj3QvHkAihZtQwuUES5xQ2qaiOwRAkJACAgBISAEhID+BLZvP4ann05iAnPtstu0CWU4mp+0CxIJQkAICAEzCRQpUgRvvPEevdiyIi7OzEYpqk2a5IMcOeqhZcvnUmx1zrdKgfG///WlYeKPNF6l9aqZRdWdP38eBgzob2YLqSYEHJsAlzelCAEh4EwEEhPdDUoLPQwd6C2J4GAYLPvd9NSsOBNwGYshOf38+WOoCNlJK5K5iIy8RWvBOJQtGw1fpghRBiiHDvlTSeLO8GzleaP1GuOQFhZyQkAIODmBu3fDoMIy3rgRAk9PDxQvng8VKhSByiEkRQgIASEgBGxHYM+eY6hcWXtOQNXDKlWA0aOZSViKEBACQiADCXTr1hs3b17Fq6/OZcSBUOTKlf7BGQwDH3/sRw+HqpgzZ1b6DZygxqJFC+nxtwrh4eEWj0a1+fXXFQwTVh/t2rWzuL00EAKOREAUII50tqSvQsAMAiVK5OUP/n2o8FVay34+6/TuDVqP+WgVJe1dgECLFjWhXpcu3WJM0RM4duwUoqKiERQUhDZtSuPTT0sblCUugEKGKARclkBsbBx+/HE9PbyWMGZ8GBfgkpA3byzfu2Hp0ixUhiaiatXStFR7BbVrl3NZTjJwISAEhIAtCVy/fg116+oT1iRrViAiQh9lii3HLLKFgBBwPgKDB49mvslKeO65oejRIxSdOsVBXZMeL8pLZNUqN0yaFMQckz0xb957NL5x/uVOFc7qgw9GMlRY2ONIzP4cGhrKUGFD0aRJY7INMrudVBQCjkbA+a8IjnZGpL9CQCOBJk3qYeXKs4x5aYWvaIpjq+ecI0dASwJwsUoHbUoK2fLWuQkULJgL6gXUde6ByuiEgBB4hMC+fafRp8/HDLsSQgVINAoVemQ3P8QYPAq3bNlDb7ETDIVXFRMmDKCS1P/xivJZCAgBISAENBBQntsS1l0DQGkqBISA3RB44YV2VOg2oEfHNDRt+j3y509kkvQYGtbF0tjOgyGY/XDiRCIaNmyG6dMH0hDUddYu5syZzfUa7Qpq5Qny/fff4623/mc35106IgT0JqBjtgC9uybyhEDmE4iNXaOP6ZSJoXh6uuPBA0oS4uOZIEGHEhISjnr1XsOff4Zpivs7ix6j168D588HcEFrJOrXr6xD70SE3gRsMYf07qPIs18CMn/s99w4Ss+S59DKlVsxfPgETJ0azofS9HuvFubmzfPkw1ZOut5PQp482dNvJDWcjkDy/FHxq/W6D3I6SDIgkwRk/phEw2vxMkRHz6Ant/bni5s3ge7dS+L333eaPqCD7pE55KAnzk66LfMn40+Eul+4dOkSPYoPQXku+Pr6oHTp0lR6lHJIjw+tc6hSpQq4cuWKLieiUKHC2LfPtuEOg4Ozyxq0LmdLhFhDQDxArKEmbYSAHRPIli0AnTu3wrhxSzFqlHVeINeugda7wOTJoJVuIGNCVrLjEUvXhIAQEAJCIDMJ7N59AiNGTGCIq3Cz4jOrvqq0Ut27xyNfvhto23YwNm36RnKDZOZJlGMLASHgVASqVy+DKVOUd531YVGSgezdC4YzrJH8Mc3/ISEhOHjwIO7cuQ0PD0+UKFEcpUqVdsiFyTQHKjuFgBDIFALKeLQQXYzVy9WLUgBFRkbphiEsLJQeNVFUKjGJpxQh4IQE3J1wTDIkIeDyBAYP7oIdO/IxDqblKCIjgTfeAC15lfIjgAnHhhm8VCyXJC2EgBAQAkLA2QlER8fi9dc/wjffmK/8SMmkWbMkNGp0izmC5qTcLO+FgBAQAkJAA4Hq1UvRQtqTFtIahPzbdOHC7HjhhS4mBcUw6/APP8xjaJpq6NChGuP1d8PJk/+jIqQvPvvsBTz1VElD3qcDBw6YlCE7hIAQEAJCwDICp06dsqxBOrXd3d1x+vTpdGrJbiHguATEA8Rxz530XAiYJODp6YGffx6PVq36M4zVDbz22gNrW9VAhR05fhxQCc4vXgQS6RmfLx9QiU4e2RmBpF8/4PnngUWL/NCxY3tUq1bK5HFkhxAQAkJACLg2gTlzVjHmcgjKachnPmBADJ599g+8+WYHCYXl2tNJRi8EhIBOBDw8PPDqqy3p0b0EgwbFWi315Ekw3EwQatasZVTGvn37mHewO0Pl3sbs2ZHMAfd4tQjDs8bGjatoXLWVIRJb0sBqPPz9JffT46TksxAQAkLAEgIxMdFc29Ee5jD5mIlcGIqOjk7+KP+FgNMREA8QpzulMiAh8IBA7tzZsH79NOzcWQMtW7ph69YHYa3q1wc++gi4ehWMlQmULQuohOeTJgHNmgGBgcDChf5o3LgjBgzoJDiFgBAQAkJACBgloOIwz5y5FL16xRjdb+5Gb2+gS5cozJ37m7lNpJ4QEAJCQAikQ6Bfv5eYYykIx46lU9HE7vh44O23gzB27LdGvcF//30F3nmnLaZNu4jRo40pPx4IplExnyuAlStDGLZmKcMeNoQKlSVFCAgBISAErCeQNWtWKK8NvYqSFagWg6QIAScloN+3xUkBybCEgCMTCAryx4IFH9ED5G307KlcGkHPEODHH0FrMPABBHjxReCtt8BEtICK8fv000oh4k5r3hKOPHTpuxAQAkJACNiYwJUrtxEQEGXwItR6qBdeSMDq1Zu1ipH2QkAICAEh8C8BX98smD59JMMUBuDyZcuwJCQor/AANGjQxaj3x86dOzBx4gAsW2a+ByCdUvjMEYM+fc6ia9fnkKAOIkUICAEhIASsIqASvytjJL2K8gApUULWgPTiKXLsj4AoQOzvnEiPhICuBHbuPM5k5rMZmzeRcXiB3LlNiw8IAGP0golsw+ie/inDYK0zXVn2uDyBqKgY7Np1AuvW7cHmzQdw48Y9l2ciAISAKxHYu/c0E+PSRFiHkjcvcOvWfYZK0c+VX4duiQghIASEgEMTUKFsJ08eydwcgdi40byhKC/xTp2C+MzQEUOGjEnVKJIJA999tweVKyHIkSPV7nQ3tG2bgCpVzmHq1M/TrSsVhIAQEAJCwDiBLFmyMHQsb6B1Kk888QQ8PT11kiZihID9EZDZbX/nRHokBHQjcPXqHXp+jKLyIwzFipkvVsXv/fnnCLRp8x2KFCmA2rU1BHc3/7BS0wEIxMTEYsmSzZg16yfcv6+s/tz48JuAmBg35pZxx717HvQqaoDevduhQIGcDjAi6aIQEALWErh58x69P/SLFZw1qxs9EKPpfu9nbZeknRAQAkJACDxGoEGDyvjtt6/pefEJQw1eZdjCcIPHt/LISFnOnwfmz/enN54/Ro2axNC4LVPufvh+7txvmS/wjiGU7sONFr4ZPjyC3iXfonv3PsiaNcjC1lJdCAgBISAEFIG3334bgwe/y/tnxjTXUFToqwEDBmqQIE2FgP0TcLP/LkoPhUDmEYiNXaOfT6GRYXh6uhti6irXxfh4fa1elcy2bQfj5ZePMAeIdcNQydJ7986Ff/6ZBR8fBmmXYncEbDmHHh/s7t0nDA/P9eqFokePaBQt+niNB/lkfv7Zg0k3/VinAz2K2usamzT1EWWLFgIZOX+09FPa2ieBWbNW02vjK37P9fn9atRIWSh/z+S4PvY5YOmV7gTkGqQ7UpcSKPPHstOtng22bj1Cz43F2LPnJLJnT6IRixuT3iYxRFYiLYkL0vPjTRqytIevr69R4cpL75lnyjO3yHUEBxutYvbGiROz8Ho/gs8a/cxuo3dFmUN6E3UteTJ/XOt822K0WudQTEwMwxRW5zXcwjiHjw2mUKHCzB27C15eXo/t0fdjcHB2WYPWF6lIs4CAeIBYAEuqCgFHIrB790nExV2wWvmhxlqmDNCoUShzhqzjYnYrRxq+9FVnAioc2hdffMdEl+H0+jAt3N8f6NYtAe3bh+GDDxaic+f9zC8zhjdT8nNjmprsEQKOSaBo0bx8WFLeGuGaB6AiX4WHu8HPL4tmWSJACAgBISAEUhNwc3Oj50cFw0vtvX37Pj13w5Elixc9vr0RFtaU8eTd+ErdNnnL2bNn6eEbp1n5oeS1aRODoUN/yVQFSPK45L8QEAJCwBEJqDBYM2fORseOHRid4b5VQ8iWLRvmzJlrc+WHVZ2TRkJARwKSA0RHmCJKCNgTge++W0wX9zDNXerRI4bhjpZqliMCHJfAH3/sxpQp3zLRZdrKj5QjVIqQiROjGB7hCPr2HZdyl7wXAkLASQhUrVoC+/bpY8h16hToVZbX4BXpJHhkGEJACAgBuyaQM2cQ79MKoFCh3GZ76x48eID5OyJ1GZfyJD5//qIuskSIEBACQsBVCdSoUQMffjjaqnCCQUFB+PjjT3ldr+Kq+GTcLkRAFCAudLJlqK5FYMeOo4ytq33MzIXF8FzhtCjQFldSe09EQmYQCAkJZ6LLzzF7dgR4f2RxGTIkhsnR92HFii0Wt5UGQkAI2DeB7NkDkTNnXhw+rL2fixb5MEmv8Xjz2qWLBCEgBISAENCDwI0bV5E/f5QeoqjwBry9E5GQkKCLPBEiBISAEHBVAl27dsNXX31N77xgeDye4MkIFFUnZ86cjO7wDUOmv2ykhmwSAs5HQBQgzndOZURCAHfvhtECIBGeOkUdqlQpEfv3nxayLkhgwoT5DH8WBqUIs6aoh9uJEyMYDmuaPOBaA1DaCAE7J9Cv36uYPJkuXxrK7dtg0t0seOmlhhqkSFMhIASEgBCwhoCHhzJ0etaspu7u7mmGyDJLSIpKKvyhCs0lRQgIASEgBLQRaN26NfM8bWco6g7Ili07VGLzx4valj17duZ76oRt23agefMWj1eRz0LAaQnotDzqtHxkYELAIQkoBUhwsH4PE8HB8QalikPCkE5bTSAmJhbLl/+JTZu0WeblywdUrx6DNWt2o1WrWlb3RxoKASFgfwRat66NGTOK4vffj/D7nUbgeBNdV7Hmhw71x/vvv8Gku5L/wwQm2SwEhIAQsAkBT89QKj8aG2Snl/9DVSpUqCgX2AL4Tnvup/h4UJniZXb4LUMn5Y8QEAJCQAiYJJDs1REeHo5//tmC7du34/Tp0wZFc4kSJVC7dm3mgaoLfxWvWooQcDECogBxsRMuw3UNAt7enoiN1W+ssbFudFH30k+gSHIIAjt3nkDNmklMSqy9u23bRuC339aLAkQ7SpEgBOyKgLLcnTFjJBo3fhO5c98FwxCbXZTyY+zYLLzGVGLyRvOsj80WLhWFgBAQAkIgXQLh4bXSTXyeUkiVKlUZZsU75Sar3x85ApQuXdLq9tJQCAgBISAEjBMICAhAs2bNDS/jNWSrEHA9AhICy/XOuYzYBQgUKBCMy5fpU65TOXHCC6VKWRkDSac+iJiMJ7Bnz3FUrardwk/1XOVV27fvZMYPIoOOmMSV3Bs37uHEiUu4cOEGElVMBylCwEUI5M2bg95iX2DQoJyYM8fTrPAo9+4Bffr44cqVqpg+fYSEQHGRuSLDFAJCwLEJ5FNuvciJM2e0j2PhwgC8+GJP7YJEghAQAkJACAgBISAE0iEgCpB0AMluIeCIBFRSq9y5s+PSJe29j4sDzp1LQvHi6oFHiisRuHr1CgoWtDykjTFGKoF6RES0sV0Ou00pPf788wA6dx6GChU60oK9N95771307v02KlXqiCZN+uD77/9AdLSO7lgOS0s67uwEihfPz+/Ddzh69GnGEw7EkiVu/M6nHvXly8C4cd6sk5WvNzB37ih4eYlDcmpSskUICAEhYJ8EevYchC+/TB1b3pLeXr8O/mb4omXL1pY0k7pCQAgIASEgBISAELCKgDxxWoVNGgkB+yfQufPzWLBgBoYNi9HU2d9/d0PTprUkPq8mio7ZWIW2USFqpKQm8Ouv26js+ALFikVgwIBEhgoDvyOP1jt7Ngzz50/DhAlz+BpIF2QLYgM9Kko+CQGHIJA1qz+++WYYYw1fpVfHEnz99S5eQ2KQM6c7EphK6Pr1RChvkY4dW2HHjiYICPB1iHFJJ4WAEBACQuA/Am3atKeBx1Rs3HgQz1oRvVDdWw4cGMTcTxMZYlefcFr/9U7eCQEhIASEgBAQAkIgNQH9siSnli1bhIDDE4iNXWPT5V9PT3dD2A9lSR4fr2/IHGVtX6tWN/z66316g1h3KlQekRYtAriIO4kxegtaJ0Ra2ZSALefQ11//gpiYmfRo0D43794FPSXyY8uW2TblYWvhhw6dQ7duoxAScpMLvWASufSPqCze337bH3XqNMOHH75uV6F+bDl/0icjNZyBQHpzKCoqBnfvhsHDw52/RdlEme4MJ13HMaQ3f3Q8lIhyQgIyf7Sf1OjosnwGyWqxwculSxepzG5MZfctVKxofj+U8uPDD/0QFtYCEyfONL+hjWrKHLIRWBcRK/PHRU60DYfpanMoODi7rEHbcD6J6LQJPGavmnZl2SsEhIDjEPD398Ho0X1pYeXHfATW9XvChCx0TW8myg/r8Dl8q+rVyzBvh78u49i7F3jyyTK6yMosIV9/vYQP++/Qkv0m1q0zT/mh+voE0+f89FMETp5cQ0+Q7zOr+3JcIZApBHx9s6BAgZwGzw/3x92kMqVHclAhIASEgBDQSqBgwUKYPftXGsnkwY8/mpf76fZtoEePrFSKN8H48d9p7YK0FwJCQAgIASEgBISA2QREAWI2KqkoBByPQLt29VGoUB0MHeprsf+BP6EAAEAASURBVBJk1iwvLn4XYgit7o43cOmxLgSqVy+NAwc8EBqqXdySJQFo06apdkGZJGHSpIVYtepHeHpGM7QckCePZR3xZMDJadMisWzZr9i795RljaW2EBACQkAICAEhIATsjECZMmXw22/bsHlzYzz3XDasWOHGvGepO6k8YceO9UWrVsF8fcb8IXPoFeiRuqJsEQJCQAgIASEgBISAjQiI+5GNwIpY5yDgyCGwks9AIt0/hg79Cvv3/8UHjggqRJL3GP8fEgKMGKFc04tzofcj+Pn5GK8oW+2CgK3dZseOnUvl2VL07x9n9XjPnwe6d8+NXbvm2VX4J3MHtH37Ubz77kg89VQEcuUC3nzT3Jap6x0+DH4fC3KxYEbqnZmwxdbzJxOGJIfMYAIyhzIYuJMdTuaPk53QDB6OzB/twK0NgfX4kY8fP4Y5c6Zg69Y/4eUVa8j9FBubhGvXEhn+MDfat++Fdu06MfdTwONNM/WzzKFMxe/wB5f54/CnMNMH4GpzSEJgZfqUc+kOiALEpU+/DD49As6gAEke4/r1ezF48ERUrhyNTp0iUKUK+BDyYG8M86QfOQIsX+6LtWu9WK878xy0cMjF6uTxusp/Dw83PlzepcIqElmyeBtCzajk5XqVyMho5rnoyTAHd1CypOVSVfi1Nm0CmOjyfTRsyEnnYCWBmZtr1XqNyZxvomdPYNMmUCmobRCdOgVizJhPGRLMCqDaDp2qtavddKcCIBs0E5A5pBmhywg4fvwSlEL56NFjiIyMQmBgIKpWrYC6dSvQOCO37rnQXAasCw9Urj/aT75eCpCUPQkPD8e9e3fp5eHJ8Id57Tr3k8yhlGdO3ltKQOaPpcSk/uMEXG0OiQLk8RkgnzOSAINySBECQsBRCISHR2Hlyu3YvXsfjh8/i7i4eGTPntWwgNCwYQ3UrFnGpNKiceMnsWfP98xdsBc///w7PvjgDKKiHvipezI+T/nyRZjwvCFd1OsbFtIVE7X4u337MVruH8Pp06e5OBGP4OBgLtxWxDPPVKQ1fDZHQedU/YyPT8Dvv++gpd0S5pW4jPz53ZE1K6gEAa5cSUTx4vkZY7kDwxHUYcgmbSEGlAfQt9++j169RmLx4nA+yJqPUik/VPi16tWfcUjlhxrp2rW7qTSMwJ07QO3a2pUfSuZLL4XxO/iHXShAVH+kCAEhIARsSWD58i2YNGkufHxCqVCPolI5Dv5ML3X/PnDo0B/46isf/obl4e/F67y3qGTLrohsISAEMoCA8vKwN0+PDBi2HEIICAEhIASEgBCwYwL6mQnb8SCla0LAWgL24gGiFB9jxkznovcWxs6N5UJsDBOTA97eMCzMHjigFmoDufjtR0v73lz4fsraIRvaxcbG0eL9Z8yd+ysqVYqnYiWc1v9JdGkHbtwA80L4YMMGL5QrV9pwvNKlC2o6nrWNDxw4i/nzl2PHjsO0NAujhRkYrskNBQvmRL16NRh26TmDR4S18u2xncof0afPJ1yUD8Vrr0WiYkVQ6fVfT5OSQOta0GPDjwqvAHzzzQjUqMHJorH88ccuegaNZxLvMIaCSl+YSnQ5aJAflTN1GHptkF1b/6U1mk6dhpL3AYZ0AIKCQM+otGqbt+/iRcWmOFavnmpeAxvWcjWrIxuidFnRModc9tSnO/CQkHBePz+lscYJjBoVgWLFTDc5eBC8n/BHhQq1+TvT/6EhhukWskcIgEYe7gbDnyTe/MTH0+pCisUEbOEBYnEnMrGBzKFMhO8Eh5b54wQnMZOH4GpzSDxAMnnCufjhUyybuTgJGb4QMELAHhQgu3adwOuvj0bXrmFc8I4zKCGMdNWwSSUZHDzYn14aFWm1P5zWltSQWFiOHr1A74EPabF/F//7X4xh0deYCLXQvnEjMHp0ABeFO+Dtt18y6X1irL2WbWfPXmPfxlLZcc3ARS3I58z5QKLq16VLoILGnTlM/FGtWjWMG9eP1qU0N3XwMm/eKibSnk2FQjgXidIfzLFj4HkJ4Pnswjn0QvoN0qlx6tQV9O79EZUatxgOKoKeHY8qX1Tz69dB7t5YutQXI0f2RocODTNsXqTTfat2V6jQEVu23Md77wEvEGG9elaJeaSRmqNPPZWdls8LH9meGR9c7aY7Mxg7+zFlDjn7GbZufHfvhtFgoz9/f24w7GbCI4p6UxKV1+CXX3rT87Qof0MmWHUPY0q2bHdOAnL90X5eRQEiSjTts8h1Jcg1yHXPvV4jd7U5JAoQvWaOyLGGgChArKEmbVyGQGYrQFSs7D59RtETIwwlSpiHXS2ufvONF3MVFMWyZZ9btICwb99pKjOGY+rUUIbVMu94kZHAO+/40tOiIT77rJ/NF7t/+WUzvWG+xqefhjFURtp9VCzmzfPEjBlZuSj/Cb0liqbdwI73Ll36J8OEfIWFCyMYN938jkZEAK+84s9FqN7o3Lmp+Q1N1EzkCtWGDfvw3XeLmDfmPL1t3KhwA2Ji3HDmTCLDmgTweM+hS5dmDq90ioiIpiKwCxV9YRg4EEzeCYZvMQHGws1KAXLwoChALMQm1e2QgKs9uNnhKbC7LilrfKX86NTpDNq2TbC4f1OmeOHixToGQw6LG0sDlyIg1x/tp1sUIKIA0T6LXFeCXINc99zrNXJXm0OiANFr5ogcawiIAsQaatLGZQhkpgLkzp1QNGjwBhe8Q1CkiOXIv/jCm2GhGjDuNlduzSjKWlMdb86ce4bwWmY0eVhFWW2+/rofmjR5jbkinnu4Xe83y5f/jfHjJzMXRQRzn5gvXYWE6tUrCEuWfM6xZU64LvN7m7rm1at30KzZmwyBFoocOVLvT29LaCjQvHkgfvttqiHRbHr1zd2vFrkuXboFNXeyZPFieJO8ThW2RI3rxRd7YtWqUM47oHBhoGNHc+mYrqfCyPXqVZBKyhmmK2XQHle76c4grC51GJlDLnW6zRrszJm/0otjDiZPjjKr/uOVlPFC+/b+DBU4DE2b0tVQihAwQUCuPybAWLBZFCCiALFgukjVxwjINegxIPLRYgKuNodEAWLxFJEGOhJgxHwpQkAI2COBQYMm0uo8zCrlhxpP//6x2LdvK7Zt4+q/GWXQoC/Qr1+oxcoPJVrl3pg0KRJTpsw3LIibcTiLq1y5cpuJ2x94QFii/FAHKlcO7Nt9hhAbZUgcb/HBM7nBiBFfYfjwMKuUH6rrKkH6hx+GM4zTZF1H4sbkI4UK5UaVKsVRtmwhp1J+KFABAT4IDeVKHEvlyuD3yfBW85/9+8EE6GU1yxEBQkAICAF7IxAfn8AwVj8yPKZ1yg81Hv600KM0gp6e39nb8KQ/QkAICAEhIASEgBAQAkJACDggAVGAOOBJky47P4GLF2/i+PGjtIC0PHREMh0PDzDpaDg+/3x28iaT/8+cuYqTJw8b4nSbrJTODrXIPmBAOBUNP6ZT07rdAwd+TgVImCHckjUSVL6KunXvMrzXz9Y0z7Q2t26FYP/+Iwwn8mAh3tqONGmShFOnTkF5k0gxj4C3txcTrPogPFzl7AD+/BNMsmpe27RqLVsWgJYtG6RVRfYJASEgBBySwNq1u1G7dqzJ/GHmDkqF/fTxCcHhw+fNbSL1hIAQEAJCQAgIASEgBISAEBACRgmIAsQoFtkoBDKXwPz5vzGHQqRZSUPT6qla9L98+QJu3LiXVjXMmrWM3hHhmo/3wgtJDBf0D2Jj49I8nqU7lULo4sUzDAOlTQnw9tvRDPG1AgkJ1iuWLO271vq//76DybejDV42WmQpi9o2bSKxcuVWLWJcrm29elWY8wSGvCt16wIrVmhDcPkycPSoD5591swkO9oOJ62FgBAQAhlKYPPm7QyHyeRgOpRGjSLw998HdJAkIoSAEDBGwNMzlPfEFiSWMyZEtgkBISAEhIAQEAJCwAEIiALEAU6SdNH1CGzdugf162tfpFeL3k89FY9du06kCXHLlr1o3FibckEdIEsWMNE4dLfYXLx4Hb1TIjQraLJlU/2LZ1iwY2nysKede/bsY7gkfRRK1aolYO9exl+SYjaBXr3aYfbsB4sD774LTJgA5jwxu/kjFVVc+4ED/fHxx/04l/nllCIEhIAQcDICBw4cR6VK+gyqUqVEekAe1EeYSBECQuARAir3x/37jZGU5MbXI7vkgxAQAkJACAgBISAEnI6AKECc7pTKgJyBwMWLtw0Jl/UYS/nyUTh0yLQCRCWyVgnXc+bU42jgwkcEDhw4o4+wf6Xs2rUftWox07oOpWbNcCqEzMuLosPhNIs4f/4qk4trFmMQULQooORJMZ9A+fJFkCtXCSxb5o68eYGhQ4GePYEoC8Pbq8WFsWOz8HtdneGvapnfAakpBISAEHAgAiEhkbA0T5ep4Sk5ISGhpnbLdiEgBDQSEOWHRoDSXAgIASEgBISAEHAYAqIAcZhTJR11JQLu7kmavR2SeQUEgDkMIpI/pvqfkJAILy/9TL+yZUvggkVYquNo2XDq1FWUKqVFwn9tS5dOYr6Tk/9tsPN3SkGlkszrUZQcJU+KZQSmTBmK8eMDcYZ6vTZtgNat/8/efYBHUbRxAP8nd+md0JsQSkKVpqiIoCJIEaSIgDQBqYKKBQQUQawIIgoKglQBEUT4BAuoFAUF6UjvvYf0nnzvHAQTkkuu7CVX/vs8593tzszO/HYkdzc78wIdOwKnTplWTpz87/fSSx44caKmxMh51bRMTEUBClDAAQW8vPRITNSm4qocb2+ZWsqNAhSgAAUoQAEK2KlAUlISoqOjJVakBsEi7bSNrBYFnEFAo5/VnIGCbaCA/Qikp2s3HT1GxiICAmQUxMim07lrEtg5s/iEBDf5wcI7863Fz8ePX8DSpRvkrvl5MqASi/nzITM3APl8YdXm6QmkpDjOh5NSpYpKHBermnw7syqnZMnQ2+/5wjSB4sWDJXbMeInLEyCzm27OAFEzQbp2hQyMABcv5l5OvCyDv2ABZDk7T5kZ1QNff/22DDbqc0/MvRSgAAWcQKBatQo4eFCbhhw4AFSvHq5NYSyFAhSgAAUoQAEKaCCQnp6O9evXo3Pnp1CtWjhq1KiG+vXrIjy8Ku6+uxaGDBmMAwccZ8UJDUhYBAUcQoC/xDjEZWIlXU2gfPlicnd5FCpUsL7l//7rKwFJjf+AoGIReHv7IC4uFn5+1p/v4EE/dO9e3qKC1OyEJUt+xbRpi6VOMbjnniRUqZKEceMggdwhwdqBnTshSwhBPljAomW7rlwBQkNlXQ0H2erVqyNt3iqxXKyPCbN7t5vEE6njIC23r2o2aBCORYsmol+/cRLA/DoGD07CL78A33wDdOsGwywdFf9GLdmilsfaJaFW1IyR+++/W9K9jPLli9tXg1gbClCAAjYQuPfe+vjzz21o1CjZ6tL/+stfBp5rWl0OC6AABShAAQpQgAJaCOyWu+H69n0WkZGRcpPmjRxF3rgRKb9nLMbPP/8ksUdrY8aMmShenN8Dc0BxBwUKQYAzQAoBnaekQH4CjRrVl7sKdPkly/e4Wu1o82adDCQYHwBRhdSvH46//863uHwTqPOpWRp161bON+2dCc6cuYKWLYdh3brp8kHhHL7/PhqjRyfJnRVAhw7AoEHAF19AXGCIiaGWIVq16s5S8n+/e7en1E+jCK35n87qFI8/3lDa6Wt1OaqA77/3F+P7NCnLFQupWbMCNm6cKTFBuqJ16yKyrFWAzCZyx+uvAy+8AAQHA/v3e2H1ah9UrNgQf/01H8uXf8DBD1fsLGwzBVxUoGPHh+RvjRfSrByzj4oCtm/3kBl0jvP32kUvOZtNAQpQgAIUcAmBGTO+kOWQn5RljU/kOviRFUENkPzxxyY8+OAD8vvI1qyH+JoCFCgkAQ6AFBI8T0uBvAR69mwjS+f4SryGvFLlf2yr/K0tX76C3HUgv8zmsfXq1V6WmArII4VphzZvVstVVEVgoHlTSY4cOSc/KA+TQY4j+OST+DwDwPv4QO4IBf73P8iyRJDBEtPqplIpz3XrvPDQQ47zg0rFiiVlpktZ+THd9HbmllLNnPHyKiFTc8vldpj7TBTw9PSQgY8u2LFjAV58cTx8ffvLh9vHZADxERQp0hXPPz9GZn8slP+fxnHgw0RTJqMABZxHIDjYH48+eh/mzbNukvnHH3ujT5920OmsvxnEeXTZEgpQgAIUoAAFCkNgzpyv8P777yEqKuesD2P1UUtlXbt2TZZN7oK9e/caS8b9FKBAAQlY9+2kgCrJ01DA1QTKlSsma0nWlOV1tqJLF8tuo1R3X44b54+JE/vky3f//dVx5UoI/vknBg0a5Js81wTqfO+95y8xEXrmetzYztjYBJnlMQKffhopM1GMpcq5v1gxYPFi4Omn1SAPZGZDzjR37lGDCGpAqHRpx4qD8e67L6B371fw44+xsjTYna3K/32yrETyyiv+Mlj0Uv6JmcIkAfWjXMOG1QwPkzIwEQUoQAEXERg3biAaN94uszeuo1Il8xut/lb/9VdRmfH5lPmZmYMCFKAABShAAQpoKHBM1jWeMGGCIdC5JcWq2SDduz8jN8xt1SRWqiV1YB4KUECWLScCBShgnwIffTRcZkME4Phxy+o3ebIn7r33QZN+oFVxQKZPH2X4kVwFTbdkmzbNQ5aWekDOGWFW9tdf/xQ9e94wa/Aj8wRqMEAti6VihMjnijw3NQgwZoy/PGQtLQfbatSoIHeOdMDQob5mLysiN55g+HAfma7bBnXqWPBLlINZsboUoAAFKFC4AmoW6KxZY+Vvu/mfYbZvV3+zgmUGyXjo9Zz9UbhX0ryzqzhup05dwqZNe2W5yD0SB+u8zLyVqbfcKEABClCAAg4sMHDgAFnyKp8fG/Jp39WrVzBp0kf5pOJhClDAlgJutiycZVPA0QWSk3+y6Tc3vd4davBBfUFMTZVfqu/Ytm49iOeeewNffRUjSxfdcdDIW/Vd87PPPGVZnjB8992HsuyRp5GUOXfPn/8j5s6dhYUL42QZq5zHje2ZP18vsQ7Ky7JUk2VJINOnKJw+fRlPPTUEa9fGGIJIGys/v/0zZ0I+lACvvZZ7SmXy6qs+MvvjCYwalf+MmNxLKdy9qo+MHv059u1bZ1gmTM2AyW+7fh2yXJOvxKN4SGYCvWDoa/nl4XHXEsjv3yDX0mBrLRFgH7JEzTXy/P33AfkMMw6DB8fgmWfS5G+Q8XanpgLTpnnK55ZgCR76HqpUKWM8MY/YlcCuXcfkZpRvZOBjF8rIZatQId1wrc+c0eHkyXSJQ1dNljjtgvvuq655vfnvj+WkiYnV5LtHoNXL7VpeA/vIyT5kH9fBUWvB/uOoV870eh85ckRWmmhhCHpueq7cUxYtWhT//ntAbvD4byEeV+tDoaEheXwazN2NeymglQBngGglyXIoYAMBNZtizpx30a9fqMzQ8EBSUt4nOXVKLQnlJ3fd3SMDEuYNfqiSe/ZsKefqj1atArBhQ97nUkfVrIuBA30lrkYEVq6cZNbgh8o/Z85KOWe8VYMfqpwuXSA/mgBqtsOdW3y8GgTwkS9492DkyN53HnaY92qg7N13B0v8k6Fo2zZQ+oPeMOiTWwOio4GZM/USVyUQHTsO4uBHbkjcRwEKUIACNhVQywRu2PAldu58AE2bBuDzz3WyBjaQknLztImJKtA55I5IT4nNFYC4uBZy88aXHPyw6VXRrvCYmHgMGfKBxMMagWbNNmLz5mj5LBaNyZNj5ZrGYunSKFnuI0ZudNmK8ePflEGwMbh6VaLbc6MABShAAQo4iMDixYvyDXhualNS5W6Pv6wN7GnqyZiOAhTIIcDRtxwk3EGB/wQKewZIZk3i4hLxzjuzZZBhPVq0SJG76BIRIStNecrkDomrJUGX3fDLL/4Sx0Mt8TRQBjAaZma16Pn48QsyaPCh/LE/K0svxcrdexmoXBkSjBRQswp27QJWr/aVL7ueMrOil/wo/7hFswuaNOknM07OwpTZDPk1pFs34K23gKpVb6ZUMUl++eVmXJLu3TsaAlerQQRn2K5di5ZZQauwaNGP8PdPQa1a6fKchthYd7mrRCfB2fQyKNQCffu2E9tgZ2gy22AjAVe768hGjC5dLPuQS19+kxt/6VIkvv76Z/lBfAcOHjwrNyykw8NDh5o1K0i8kIbo1u0x+Pn5mFweExauwPnz19Cu3XAZ1LgmwepT872RRc3EXb5ch48/DsKyZRM1G+Tivz+W9wPOALlpxz5keR9iTsid/Hmv5kAjxxdo0eIxiZP6jyYNUb9FjBo1Rpb6HH67PFfrQ5wBcvvS80UhCDjHr4GFAMdTuoaAvQyAZGrHxydizZqt2LZtl/yAcEzuokxFSEigxHaohUceaSBxNG79+p+ZwcrnI0fOyR18v8gffbWW80XDUl3Bwb4S6yNczvcg2rS5z+I1utWSTjVrdjbcHWhlNQ3Z33335jJYVau6yR2mfti61Q2NGtWRDxi9ZFCkrBansMsyrl+PkvaelMGPBMNyZ7VqVUSJEiF2WVdWyv4EXO1Dt/1dAcevEfuQ41/DwmwB+09h6lt2bnVTzsMPD5Qbbi7JZ0HzVopVM36GDSsiM4ena3KDBvuPZddQ5eIAyE079iHL+xBzcgDEFfpA9eoRuHTpkmZNbdeundzIOPd2ea72bxAHQG5fer4oBIH/Fp8rhJPzlBSggHkCKr5Gp04PGR7m5bQstVqDe/ToZy3LnE+upKQUWTIrn0RmHC5ZEhIfo67MjGmAXr2qSJyMKjIrwvnvJi1ePESWnihiNI6MGYRMSgEKUIACFKAABfIUGD16mixrdc3swQ9VaP36Ksj9DRkE+RCLF8udK9woQAEKWCiQJtP9jx49ihMnjks8mzQZVC2KGjVqyvc/fwtLZDYK5BRITzdvoD9nCdn3qL7KjQIUKBwBDoAUjjvPSgGXF9DrdbfXAdcCIynJTWakPIj+/VtrURzLoAAFKEABClCAAhTIInDo0BmZYbsFP/10K5BLlmOmvuzQIV2WwTqMP//cJzN1a5qajekoQAEKGAT2SjCpWbM+wpYtG2U5PZ0s05wkKxJkyF36nnIzHBAQUAK9e78ky/R1yBZsmnwUsEQgODhIlhm/bEnWXPOULl061/3cSQEK2F6AAyC2N+YZKECBXATUAEh6uh7JyTdjmeSSxKxdhw/7oXt3513qyiwMJqYABShAAQpQgAIaC8yY8S0GDozLN+ZHXqdV4diGDInF9OmLZACEs0DysrLVMb0+GmlpAbYqnuVSwCYCcXFxmDBhBHbvXi2xHW9ITKGb8Sn/O1mc4eXRo5GYOfNlzJjxoawIMB/VqlX/LwlfUcBMgXr16uPIkSNm5so9uZ+fHx54oFHuB7mXAhSwuYC7zc/AE1CAAhQwIlC7dkVDQHUjh03erYJrbt/uJrFQKpuchwkpQAEKUIACFKAABUwXWLt2q8y2tX45kEby+8/OnUfkRph000/OlJoIqNgfUVHNNCmLhVCgoAQiIyPRocPDKFduGX744QaaN79z8OO/mlSWr4MffhiLDz44JisDtMHvv6/77yBfUcBMgfbt2yMwMMjMXLkn9/T0lIF/DoDkrsO9FLC9AAdAbG/MM1CAAkYEunVrh6+/9jNy1PTd27YBFSvehaAg68sy/axMSQEKUIACFKAABVxD4Pr1GFlaJh3y+43Vm5oFUqWKGw4fPmd1WSzAPIG0ND+JG+cmg09u8mxeXqamQGEIpKamokePNhg06LjMHkuCTmdaLerVgyy3F4mxY/tjz549pmViKgrcIfDII4/C29v7jr2WvW3Q4B6EhoZalpm5KEABqwU4AGI1IQugAAUsFWjRooHMAPGVAHaWlgDDl7eJE/0koGYPywthTgpQgAIUoAAFKEABowJnz16Ru6+NHjb7QLlyaVBlcqMABSiQl8DUqR/g3ntPon1784NHlywJWW4vEi++2FOWXZZ1l7lRwEwBnYy4jR//tswCCTQzZ/bkwcHBePfd97Lv5DsKUKBABTgAUqDcPBkFKJBVQH2g+OSTEXjhBT+LA6LPn69D6dJ3o0mTu7MWzdcUoAAFKEABClCAAhoJuKlpG1APbTY1+8BQpDbFsRQKUMAJBa5fvy6zOGZjxIh4i1tXqxbke+JlLFo0z+IymNG1BTp16oQGDRrAy8vLIoiAgAAMH/4ywsLCLMrPTBSggDYCHADRxpGlUIACFgo8+GBNPP54GwwY4Gv2IMjq1e5YsKA4Jk9+2cKzMxsFKEABClCAAhSgQH4CZcsWxalT2q2ZdPq0DmXLFsvvtDxOAQq4sMDixXPQrVu8LEFkHUL//gmYP/8zWTlAu3/DrKsRczuSgLoBYP78hahRo6bZy2GpmSNdu3bD4MFDHKnJrCsFnFKAAyBOeVnZKAo4lsDIkb3lroon8cQT/jh4MP+6JyZC1nP1xuefl5NAeFMY+yN/MqagAAUoQAEKUIACFguEhAQgPl4H9RnM2k3FPj96NEPigJSxtijmpwAFnFjgl1+Wy9JXSVa3sHRpSAyjWJw/f97qsliAawr4+PjI7w6r0bNnL6jlrPLb1EoXRYoUwTvvvIv33ntfZjxqN4Myv3PzOAUokLsAB0Byd+FeClCgAAXUB4LXXuspMzkm4PnnS+K55/zx++9AdPR/lZD4d9i/H5g40RNNmwagZMmOWLduOooWDfovEV9RgAIUoAAFKEABCthEoGXL+7FqlfU/4mzYAFnTvxrc3flV1CYXioVSwAkE1GyNCxcuooxG46R16iRj9+7dTiDDJhSWgFoCSw1mrFr1P7Rp00YGQkIMgxweHp6GAQ4VLF0FOVeP557rjz/+2CwzmJ4prOryvBSgwB0C+jve8y0FKECBQhO4994IbNkyBxs37sGSJT/g7bcP4erVGKSlpUGvz5DgY76oWjVcBkHa4JFH6sg+XaHVlSemAAUoQAEKUIACriTQv38n+TFnIzp0iJXPYJa1XK1A89ln/hJUtqtlBTAXBSjgEgLx8fHw93fTLFZQmTJxuHjxgkvYsZG2FVBLYc2bt0BmRCbi33//xaFDh5CUlGgIlF6zZi1UrlwZagYINwpQwL4ELPzoal+NYG0oQAHnEVCzQWrXDsNPPwUhLi4Rjz6qQ926iShXTq3ZGiXrT2+TYHj78eqrOpkS/bA895APG37OA8CWUIACFKAABShAATsUqFSptMzCbYJp09bihReSLarh4sU6lCpVEw0bVrMoPzNRgAKuIaBmiGkZs0MNvnLWmWv0nYJqpZrxUb9+fcOjoM7J81CAApYLcADEcjvmpAAFbCDw889bZVBjMvr3j8GGDWm5BL3LQO/esbIONSQA+v/QuPEGfPLJSPlCfrcNasMiKUABClCAAhSgAAUyBd56q7/cnLJLZuReQMuWEszDjG3LFkj8thD8+uvLZuRiUgpQwBUF1I/LcXFuUDGDtFgt79ixQDz+eHlXpGSbKUABClBABLjwKrsBBShgNwJLlqyVJRE+wLff3pBBjtwGP/6rqq8vMGBAGhYvjpT4IW9j5co//jvIVxSgAAUoQAEKUIACmgv4+HjJZ65JEretJKZO9ZBlSvM/hbrzet48PUaMKIoVKz6SNdMD8s/EFBSggEsLqFUBwsIq4MgRbRh27nTH3XfX0aYwlkIBClCAAg4nwAEQh7tkrDAFnFNg+/bD8mV6BpYujTMr2F15uZHn229jMW7cFOzbd9I5cdgqClCAAhSgAAUoYCcCxYoF47ffPkdkZDO5o9ofy5e7ITmXFbHU4MiPPwLt2vlL8OH78fvvM1ChQkk7aQWrQQEK2LvAE0/0wjffyF1vVm4HDwK+vqUMwamtLIrZKUABClDAQQXcHLTerDYFCkQgOfknFXjCZpte7y6B3dwM65umppq3jIDNKlUIBScnp6BRo36ypvQlVK9uWQW2bwdGjSojAdRnulTQMfYhy/oLc90UYP9hT7BWgH3IWkHXzs/+4/jX/9ChM5g5cxl+/vlvBAenywDHzeVqzpwBLl92w0MP3Y2BA7vInddhmjeW/cd80ri4BvK9QycP8/M6Yw72Ifu+qioQ+sMP15bYkNdk5pjldR00KEAGa6eibdsnLS8kl5zsP7mgcJdZAq7Wh0JDQ/gbtFk9hIm1FGAMEC01WRYFKGCRwPLlG/HAA9EWD36ok0r8MURERGLNmq144on7LaoHM1GAAhSgAAUoQAEKmC4QHl4Okya9JA814HEDZ85cNtzYU7p0UZQuHWp6QUxJAQpQ4A4BX1nzePjwcbJ83igZaI2WGwfvSGDC23Xr3OTfpiry/bCdCamZhAIUoAAFnFWAS2A565VluyhQgALpEp3u2LHz2LbtEHbuPIqYGIlQbsY2e/a36NMnwYwcuSft1y9ePhwvyf0g91KAAhSgAAUoQAEK2EygePFguSGlKho0COfgh82UWTAFXEugc+du0uCG+OgjH7Mbvncv8MYbRfHJJ/MMqy6YXQAzUIACFKCA0whwBojTXEo2hAIFK6AGPX79dSe++GIxDhw4hYoV3VG0aDpSUtwkWF0G0tO98NRTj+HZZ9uiRIkQo5VLSkrG1avXUKmS0SQmH6hVCzh+/LycOx3u7hzfNRmOCSlAAQpQgAIUoAAFKEABCtiZgFou+rPPFmLQoK4YMmQb3nsvBoGBeVdSLfG2ZIken34aijlzVkp8ybJ5Z+BRClCAAhRwegEOgDj9JWYDKaC9gJrtMWDABBnYuIShQ+MMy0/dOSU5OjpGPnh+i8ce+wGDB3dD//7tch2U2L//NMLDLZjPbKRZYWHuMhvlAqpUKWMkBXdTgAIUoAAFKEABClgrkJKSKrHX9mD79v3y2esEUlNT5bNhMdStWxtNmtSGmhHCjQIUoIC1Ap6enpg1axkWLpwt3y3fRZcu8XjmmST5NyZ7ycnJkHhEwJdfBqNUqfvwww/TERJi/Ea87Ln5jgIUoAAFnFmAAyDOfHWdo226qlWrqnmvXeVRTx4q/FmiPE7L43e50//zo0eP7pfX3ApIYP363Rg2bAI++CAGjRsbP6m6M6d//1R07RqDN9+cj27dtmHBgvHw8Mj+z05UVBxCQ7ULAB8SkoGoqFjjFeMRClCAAhSgAAUoQAGLBRISkjB58tdYvPhnNGyYJktexcms3wzodMDFi8A//6yTu7Q9JLZbVYwdO5g3pVgsrW3GxMRqUiBnSGurytIKSkDNBOnRo58EMn9KbrKbKwMg82XZ5UiUL+8Ovd4Nly6lIyHBA/ff31j+/XlR/v2pUVBV43koQAEKUMABBLS77doBGssqOpZAjRo1isidZD9kZGRkRrSOkhack4daALSCPFT/TZEPQy8cOnToc3mt+Zac/JNMoLXdpte7G9YjlTbKXXPaDQLYqsYqvsezz74uHzpjUNbMmcQffuglgTHryTTksdmqt2XLfonbMVamKMdk22/pmz59AjFmzPuoXTvM0iIcKp+j9SGHwnWByrL/uMBFtnET2YdsDOzkxbP/ON4F3rHjCJ57bjzat49Cv37J8PfPvQ1qCZrffgPeessfffs+LUvXdNJ8DX72n9zt79yr08UiOrqp7HaXAPVu8rgzheu+Zx9y7GufkpKC8+fPIy0tTW6oC0VQUFCBNoj9p0C5nfJkrtaHQkND+Bu0U/Zkx2gUbwFxjOvkkrVMTk6ec2vwQyazov/hw4eLyKOGPMJk0EP9/L5GHh6SZlqVKlUauiRSATZaxeoYMOBtzJhh/uCHquarrybJXTp7sGzZ+my1jogoh4MHtfs7ePRoBu80zCbMNxSgAAUoQAEKUMB6gT//3Ic+fUbJZ8ErePFF44Mf6kxqadRHHwV++ikWmzYtlptTbHKvkvWNcqESOPjhQhfbRZrq4eGBu+66C2FhYQU++OEixGwmBShAAacR4ACI01xK52pIREREBRnkaKtaJc9TZdDjS3l5e4qEzPg4L/u7yCP+ZhK3QSotN9sJzJixUr7I3kANC2cTqy/CEyfGYdy4LyRQeurtioaEBCApyUPuTLu9y+IXV64Anp6+8PHxsrgMZqQABShAAQpQgAIUyC5w4cI1DBz4NhYtikFERPZjeb3z84PM9I3H/v3rZP1+WZyfGwUoQAEKUIACFKAABQpYgAMgBQzO05ksEJ6ZUqa0bsx8nfVZBkFiZPbHoVv7bqfPmoavtREQZ1m66nuZAZJkVYHFikGCYibh++//yFbOU081xzffZI8Nki2BiW8WLvSQ9WCfMDE1k1GAAhSgAAUoQAEKmCLw/PMfyCyOGFlv35TU2dOo2CBTp8ZL/LjZEiPkevaDfEcBClCAAhSgAAUoQAEbC3AAxMbALN4yARn0uJSZU6fTeWe+vvNZfpj3uLXvwp3H+F47gf37T6FcuRQULWp9mU89lYCVK3/JVlCfPm1lgMXHqlkg164BS5f6SHC8x7OVzTcUoAAFKEABClCAApYL7NlzXD6jnUDr1rcnY5tdWEgIMGhQHKZN+9bsvMxAAQpQgAIUoAAFKEABawQ4AGKNHvPaTCAwMPBfKfyEOoEMcnTL7USVKlUqJ0tgGSbhy/MPuaXhPm0EVPDzunUTNSmsZk1g376T2coqUSIEw4b1xMiRPhYFZkyX7+OvvOKL0aP7IzjYSDTObGfkGwpQgAIUoAAFKEABUwRmz14ugcxjTEmaZ5pOndLw3Xe/GQIW55mQBylAAQpQgAIUoAAFKKChgPVrzmhYGRZFgUyB7du3p1StWrW/vF8ljyclyPl0mQkyxc/P70RsbKy3DHg8kJ6ePlmOqT68TpbDmifPTrGp5aa2bj0oQSP/wPbt+3D58g24u7uhQoWSqF+/Djp0aIqKFUsWaFvPnDknAeZULHrrNy8Jz5GWlpKjoL5922Dz5p0YO3aHxAlJNATPzJEolx1q8GPECB+ULHk/nn5aom1yowAFKEABClCAAhTQTGDz5j14/XXri/P1BSpXzsDhw+dQrZoFa2lZXwWWQAEKUIACFKAABSjgggIcAHHBi+4oTZbA5+sqV678oLu7+0gZ8BggAx6DYmJu3n2mBglkOymPUR4eHpPkOU0emm96va0nSUlkcMPmBnWuX3/dKbMgPkapUnFo2zYG7dpBXqsBA+D48YsyILIH3bsvR4kSd2HKlFcRFiYHHXBTAdFzs50z5w288MIkdOq0RdoXJ8tu5d2448eBF1/0R8OGTWVd6edloMjW1yvv+hTO0ex9qHDqwLM6rgD7j+NeO3upOfuQvVwJx6wH+4+9X7eUlFQkJyfCX6MJtrVrJ8lM4BOoVauCBk1n/zEN8aaT+vyt07niZ2XjSvId03BQPef23cR4Th6hAOSGPfYf9gPrBGzVh+SmYZw/f16tpiK/HZWQVTKCrasoc1PACQQ4AOIEF9GZmyA/aNeXf7SryR8G9WldjX6clYeKCVJBHqXl0SglJWWNPO+Wh+Zb5h8kzQu+o0CJeYLhwydj9+7NmDEjVgY27kggb+vUUY90wxIE69fvkwGS5/Hqq33Ru3frnIk13lO+fBkcOaLCreScuWHuqZIkjrq7u8ftD4xZ8+v1elkbegR+/nmbDPR8LHcHJqBjx1hD21UAdbVdkugwO3cC334bgBMnfA0DQU2a3H3zoAv/9+bn75sfwl2YgU23UID9x0I4ZrstwD50m4IvLBBg/7EArYCyJCQky+CHdp8vgoKSERMTn+vnQEubxP5julxBfbcxvUb2k5I29nMtHLEm7D+OeNXsq87W9qFz585h+vTpWL58ORITE2/fHCo3EssArx7NmzfHSy+9BFldxb4aztpQoIAEOABSQNA8jfkC4eHhn8vgx0D5Q3BdHp1lmavvpBTDTA+J/1FclsQaK+8Hy+PRiIiIlgcPHlwvrzXdbs000bTM7IW5yah8ugTuHisj87tlXWT1hyp7itzeNW0K/PhjDHr1+hLx8YkSVLJDbsk021e/fhUZcPCR8qwfANm/H6hR4y7D3QjGKti8eQM89thCw4yYlSvX4eOPD+LGjVhD8tDQAFkKrJrEDGmOhx6qZfgCbfvrZKym9rDfTQxUrBxVF8N/7KFSrIPDCLD/OMylstuKsg/Z7aVxiIqx/9j7ZfL01MsPKdp9vkhI0MPLyyPPz4Gmm7D/mG51M6Vrf2bOqZX1B0fa5PThnrwF2H/y9uHR/AW06EPJycmYMGEC5s+fj+joaKNxtr7++mv88MMPaNGiBSZPniw3N2g0tTP/ZjIFBexCgAMgdnEZWIk7BST+RzP5EDpQ7Zc/CoNk8OPbrGmOHTt2Wd4PkXRqFsiTMqo9XZ5rykMiQmi3paZqWlyOiqmp1p988g28vffgzTdNj3uhCgoIABYuVEtlLUS9etVlUKBqjvK12lG1ajmcPKnH9etAkSLWlbp8uQ+eeKIZTLFt2rQO1MPYlpamvpBr96Xc2Hnsef/N6frqzswMk0ztuS2sW8ELsP8UvLmznZF9yNmuaMG2h/2nYL0tOZu6azQlRS/LYAGenpaUkD3PgQM+aNWqvCafWdh/stsae6fT3fysrG6WMeXzt7FynHG/6kPqB0g1+EEbZ7zCtm0T+49tfV2hdGv7UFRUFNq0aSUrY5xAQkJCnmTq37kbN25gxYoVEnt1M1avXoMyZcrmmYcHKeBMAibca+5MzWVbHEigx626xsjgx9I86q1mhaitmsQLcbh1kE6evIjZs7/F228nGO7iv9kU0/+rgklOnRorM0DeMTrSb3ppxlOq2Bq9erXFrFnWffO9dk0i1q/zlEDuDxk/GY9QgAIUoAAFKEABCtiNwN13h0kcOuuro2La7d6djpo1K1hfGEugAAUoQAEKuLCALAVvGPyQ2Ln5Dn5kZVIzRs6cOSM3I7SEGkDhRgFXEeAAiKtcacdrZ2Z07wv5VP1q5nH5kT4zT+Yuu3/+9NNvMHRoLPz8LK9q9eqQQJKxEjfjH8sLMSGnWmZrzZpgHDxoQmIjSV5/3Q+jRvWTOwhVPBFuFKAABShAAQpQgAL2LtC7d0fMm2f9Uhnr1gEPPlhXlsCy7oYae/di/ShAAQpQgAK2FnjnnQmySsdJmb2WatGpLklw1WHDhlqUl5ko4IgCHABxxKvmGnWOvNXM8vJstJ/KND513LDJMliyQJPjbCoY1f/+t0mWsLJ+CadeveIwZ84ymzbe29sTX3wxGgMG+OPiRfNPNXWq+rJbDV27NjM/M3NQgAIUoAAFKEABChSKwMMP18GxY4HYs8fy08uNqpg40V9u/HnG8kKYkwIUoAAFKEABnDt3FgsWLJB4sPEWa6gZJH/8sQn//GPbG2ktriAzUkBjAaM/LGt8HhZHAXMFNtzK4C1xPtrmkbn9rWMqQvbOPNLZ3aGjR88jLAwS/8P6qtWRMBn795+2vqB8SmjQIBzvvvsaOnUKwN9/55P41mH1N3nkSG9s21ZVBmneNKyza1pOpqIABShAAQpQgAIUKGwBtRTq9Omj8NJL/oiLs6w2H37ohebNH+PyV5bxMRcFClRA/TB69uwZWSbntFU/sBZopXkyCriQwOeff46YmGirW6xigkye/JHV5bAACjiCAIOgO8JVcsE6ent7z0tMTHxdmq6iMs2uUqWK7siRI9/La1k9GIiIiAiVGRRj5WUL9V62j44ePZp086Vj/Pfff0+ienWJKKnBJt9LERKSIUHKYyRIeYAGJRovokWLe3HXXZPRv//bqFLlKp57Ll6+zOZMHytDUsuW6STGiS969uwgwd47Q6fT5UzIPRSgAAUoQAEKUIACdi1Qt25l9OvXA927z8f8+XEIMPHjpgq8PX26J/buDcN33/Wx6zaychRwZYHr169j8eI5WLVqkfywGonSpd2hvmNeupQuLD54+OEWEhNyKCpWrOjKTGw7BexC4PvvV2gWA3br1q2GsvhbjV1cWlbChgIcALEhLou2XGDPnj1xMsjRSgY5VkkpFdzc3JbJTJAYeX1WHp6yX33yko9khm2WBH56+9Zrh3lKSEiW2B+G8RxN6uzr6ybBr9QYkInfSK04a0REOfz+++dYvXqrBHBfjNOnL8hgiDuKF09DUpIbDh92kw/OOgl2/rAs89UeZcsWs+JszEoBClCAAhSgAAUoUNgCzz3XTuJ3eKBlyzl4770YNG6cd41keXG8+qqffN6tLjfFvMEYcHlz8SgFCkVALcs8a9Y0man/MZ55Jh5ff52EYnd8dYuLi5TvdPPkxrcVqF+/Jd58c6L8f21FEMtCaSlPSgHnEIiVO03lZmHNGqNmecrNxAgPD9esTBZEAXsU4ACIPV4V1skgcPDgwb21a9eumZCQ0EcGQJ6UnWqeQWV5qGkTx+WxWR5q8GOTPDvcFhDgg6goFQxcFkXWYIuOzoC/v48GJZlWhLpDoG3b+w2P1NQ0CY5+RmagRMPDQy+DIWVQtGiQaQUxFQUoQAEKUIACFKCAQwj07NkKDzxQB8OHf4j33z+HLl1i0aBBhnz2g8z0hSFO3O7dkB9L/bB7tzfeeKM/OnZs4hBtc8ZK6vXR8n2jmTM2jW3SQEC+Z6Nv3w4oU2Yf1q2Lle+SuReqxjq6dElH5843MHPmd2jd+g8ZKFkj+dRiDdwoQIGCFDh37pzMzsq8F9j6M6sl79RydxwAsd6SJdi3AAdA7Pv6uHzt1EwQQfj01sOpPGrXDsOnn2rzv6AKLBkX546goMK5E0ev13FNZ6fqnWwMBShAAQpQgAIUyF2gcuXSskzOFBw5ck5mdqyTgZA9EiT9IjIy0uUGmADUrVtN7iRvLHeU19P0R5rca8O9xgR0utjbgx8ZGW5yfYyl5H5XFFAzP9TgR5MmOzFwoGkrSavfXFXaGjXOoGvXx7Fy5SZZhjnEFfnYZgoUmkCGDf4xt0WZhQbEE1PAiIA2v74aKZy7KUAB4wLlyxeXu+R0iJbYVYGBxtOZcuSvvyDTkauakpRpKEABClCAAhSgAAUoYLWAmvH7+uu9rC6HBdhGwM1NxW6ADHxw8MM2wo5d6uzZ0yXOxz6TBz+ytlYtfzd48CWMHDkQM2Z8k/UQX1OAAjYWKFWqlPy7rt2ItoeHB1SZ3Cjg7ALazZtydim2jwIaC8iyXjKVuDmWLrV+HPKrr/xlTdbOGteQxVGAAhSgAAUoQAEKUIACFKCAMwlERkbiq68mY9y4WIub1aVLKm7c+BubN/9pcRnMSAEKmC8QFBQEvd7635Ayz6xmg4WHR2S+5TMFnFaAAyBOe2nZMEcQGDSoo3z49MW1a5bX9k/5zHn9ejFZj7mG5YUwJwUoQAEKUIACFKAABShAAQo4vcCiRV+hW7d4ozE/TAGQe/nw0ktREkD9Q1OSMw0FKKChQLNmj0HdUKvFpmJ/qFkg3Cjg7AIcAHH2K8z22bVAsWLBEhxyIF54wQ+pqeZX9coVYMSIAJl6/IZmfwDNrwVzUIACFKAABShAAQpQgAIUoIAjCKxatUhWIjAt7kde7bnvPmD//j1ISrK+rLzOw2MUoEB2gWHDXpD4r8HZd1rwTs0meeWV1yzIySwUcDwBDoA43jVjjZ1MoFu3x1C79mOy/qov4uNNb9zZs8DTT/tjwoThUMEouVGAAhSgAAUoQAEKUIACFKAABYwJpMpdd1FR11G8uLEUpu9XN6DXrOmOAwcOmJ6JKSlAAasFqlSpgubNm8PT09Pistzd3RERUQ1Nmza1uAxmpIAjCXAAxJGuFuvqtALvvDMIjRp1weOP+2PDBhWs0HhTk5OBuXP16Nw5GO+/Pwpt2txvPDGPUIACFKAABShAAQpQgAIUoAAFRODSpUsS/FynmUVYWCJOnz6lWXksiAIUME1g4sSPUKJECdMS55KqSJFQWcJuNlcSycWGu5xTQLvIOc7pw1ZRoEAE1PqNQ4d2llH8+/Hmm9Pw1lvH0LJlEurWTZYPqEBaGnD0KLBjhw/WrtXLsUbYuLEfgoP9C6R+PAkFKEABClCAAhSgAAUoQAEKOLZARkY65MZvzTadLgMqiDI3ClCgYAX8/f2xevUa+W2opQxsXpQl1U1bU13N/FCDH99995381sSVRAr2qvFshSnAAZDC1Oe5KXCHQHh4OXzzzfs4f/4afvttJzZt2oWLF6/Kh1Q3VKxYAQ8/XAvvvFMPfn7ed+TkWwpQgAIUoAAFKEABClCAAhSggHGB0NCiuHxZuwGLc+e88NBDlt+FbrymPEIBCuQnUKZMWfnN6A+5mfZ5/PnnH7hx40aeWQIDA2XZqwjMnj2Hgx95SvGgMwrIqo3cKEABYwLJyT/lsRiVsVym79fr3Q1TDjNkzavUVO0+iJpeA6Z0dAH2IUe/goVbf/afwvV3hrOzDznDVSy8NrD/FJ69M5yZ/Sfvq6jXR0ush2aytK5bnsvr5l2Kcx911T7UpEkNrFlzHnIDudVb06Yh+N//9khZGhRmdW0KtgBX7T8Fq+zcZ9OyD23btg2TJ0/C1q1/Q6fTISUlxYCn1+sNs7TUwMcrr7wqMT8eLrRlr0JDQ/gbtHN3abtuHWeA2PXlYeUoQAEKUIACFKAABShAAQpQgAIUoIA2Ag8/3BKrVs1Bt27W3YB36BAQGFjKJQc/tLkSLIUC2gncc889WLx4iSyfnibLpx/FmTOnZfA7A6VKlUJ4eAQ8PDy0OxlLooADCnAAxAEvGqtMAQpQgAIUoAAFKEABClCAAhSgAAXMFXj22aHo02c5nn76htwpbm7u/9LPmOGPZ599+b8dfEUBChS6gJr9ER4ebngUemVYAQrYkYCG4a/sqFWsCgUoQAEKUIACFKAABShAAQpQgAIUoEA2gbvuugsNG7bBjBle2fab82bLFuDQoXJ44ol25mRjWgpQgAIUoEChCHAApFDYeVIKUIACFKAABShAAQpQgAIUoAAFKFDwAmPGfIClS0tg/Xrzz336NPDyy0UwZcp8Q6wB80tgDgpQgAIUoEDBCnAApGC9eTYKUIACFKAABShAAQpQgAIUoAAFKFBoAr6+vli4cA3efLMsvv5aL7ECTKuKmvnRpUsRTJq0EJUrVzYtE1NRgAIUoAAFClmAMUAK+QLw9BRwNoHU1DRs335YHodw9uxZQ+CtsmXLon79cHlUleBb/GfH2a4520MBClCAAhSgAAUoQAEKOJZAmTJlsHLlJowaNQTff78ZL754Aw88ALi55WyHCnj+xRf+suxVWcybt4CDHzmJuIcCFKAABexYIJc/bXZcW1aNAgUskJz8k4n3wlhWMb3eXT5guhkGCVJT0y0rxE5yxcUlyjToRVi8+GfUqZMujzjcddfNNp0+7Y6dO/2wa5e73DHUQj5cd4W/v4+d1Nyxq+FMfcixr4Rj1p79xzGvmz3Vmn3Inq6G49WF/cfxrpk91Zj9J++roddHIyqqmXzPUN818k7rqkfZh/678n/9tQWzZ0/Enj07UaOGO8LCkmR5qwycO+eFffuAoKDSEvB8uCHmhwqyzA1g/2EvsFbA1fpQaGgIf4O2ttMwv8UC7HwW0zGjKwhwAMS0q/zHH3vx/PPvo3PnaPlgnIKAgNzzxcRA7hjSY8mSQEydOhIPPVQ794Tca7KAq31oMhmGCU0SYP8xiYmJ8hBgH8oDh4fyFWD/yZeICfIQYP8xjpOYWA1paQEc/DBOZDjCPpQTKCUlBQcPHsTJkyel/6SjePHiqFmzlty85p8zsYvvYf9x8Q6gQfNdrQ9xAESDTsMiLBbgAIjFdMzoCgIcAMn/Kq9evQVvvTUJs2bFolKl/NOrFCdOAH37+mPMmJfQtm0j0zIxVa4CrvahKVcE7rRYgP3HYjpmvCXAPsSuYI0A+481eszL/pN7H+DMj9xdctvLPpSbCveZKsD+Y6oU0xkTcLU+xAEQYz2B+wtCgIvxF4Qyz0EBJxXYv/+UDGJMxnffxaJYMdMbWbEi8O23sWjf/mNUrFgatWrJDm4UoAAFKEDF4kiVAABAAElEQVQBClCAAhSgAAUoQAEKUIACFKAABTQUcNewLBZFAQq4kEBaWhoGDJiAyZNjzBr8yCQKDYXEDInFwIEToAKnc6MABShAAQpQgAIUoAAFKEABClCAAhSgAAUooKUAB0C01GRZFHAhgdWrt6Jq1Wu45x7LG12vHiTIXiR++GGL5YUwJwUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBXAQ4AJILCndRgAL5C8ycuRj9+sXnnzCfFKqMmTOX5JOKhylAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKmCfAGCDmeTE1BSggAmr5qxMnLkjsDus5qlcHzpy5ZFgGS6/XWV8gS6AABShAAQpQgAIUoAAF7EJgx44dWLVqIXbt+hsXL16Gm5sbAgL8Ubt2PTRr1hGPPdYcOh2/A9jFxWIlKEABClCAAk4qwAEQJ72wbBYFbClw5Mh5VKmi3QQyVdahQ2dkOawKtqw2y6YABShAAQpQgAIUoAAFCkBg9+7dGDnyORQpchVPPx2J554DypSBDIAAkZFXZEDkBFauXIfx470wYsS7aNeuYwHUiqegAAUoQAEKUMAVBTgA4opXnW22S4HExGQsX74B69Ztwt69J5CcnIz0dKB06RA0aFALXbq0lDulwuyi7jduxMiXGamcRltoaLp8EYrVqDQWQwEKUIACFKAABShAAQoUhkBGRgY+/fQDmfUxA598cgM1a+asRUgI8PDD6hGFy5eBV199GWvWLMXUqfPh5eWVMwP3UIACFKAABShAASsEOABiBR6zUkALgXQZ5Zg2bbl8UViCxx9PRvfuiYalpXx9Afn+gHPnorB160m89tp6GRApJl8MXkdERDktTm1xGZ6eHjJAI7dvabQlJkK+7HhoVBqLoQAFKEABClCAAhSgAAUKQ2DSpPHYv382fvwxBp6e+degeHFg7two+S60Eb17P4n581fBw4PfC/KXYwoKUIACFKAABUwV4ACIqVJMRwEbCERGxuCpp0YiLOwsfv01Af7+2U+ipoiXLXvz0aFDjAyExOCZZ17C4MG90LfvE9kT3/HuyJFz2LLlX/z77wHExcVJ2f5yB1Z1PPBADTlfqTtSm/e2SpUyOHxYRmc02g4fBqpWlYZyowAFKEABClCAAhSgAAUcUmDDhvXYtGmOzGqPkUEM05ugvvMMG5aIa9f2yKyR9/DKK2+anpkpKUABClCAAhSgQD4CHADJB4iHKWArgZiYeLRs+YKsh3sBHTummXSae++FTA+PRY8ecw2ByPv3fzJHvjVr/sZHH30l6+tGonHjBNxzT4phYCU6Gti3bx1mzfKWu7GKyoySfhJ4sF6O/KbsCAjwldko3lBLYQUHm5LDeBpVr5QUTwQF+RlPxCMUoAAFKEABClCAAhSggN0KqOV7R40ahEWLoswa/MjaoFGj4tGixRw8+WQ3VK5cOeshvqYABShAAQpQgAIWC2i3ho3FVWBGCtivQHLyT9pNc7ijmQMGvINq1bagX7/UO47k/1YmdOCJJ/zx5Zcf4u67b8YFUQMqQ4a8L7E0/pVggnESpNx4Ofv3A2+84Ye77qqHKVNehq+vt/HERo5MnLhQjizBoEHm1z9rkTNn6mU5rackSGKvrLv52kQBvd5dBrvcZLm0DKSmaheXxcTTM5mDC7D/OPgFtIPqsw/ZwUVw4Cqw/zjwxbODqrP/5H4R9PpoREU1k8+G6vNh7mlssXfFiuXYtu1FvPeedXH9Vqxww19/dcH770+3RTWzlck+lI2Db8wUYP8xE4zJcwi4Wh8KDQ3hb9A5egF3FJSAe0GdiOehAAX+E9i4cQ9Ontwhy1hZNnjgJ5MlJk+OlQGPdw0/fEdFxaFVqxdw77075K6rvAc/VC2qVweWLYuTO6v+Qtu2wxEfL0E4zNyeffYJWaPXR75gmZkxS3I1+2PuXB88+2zbLHv5kgIUoAAFKEABClCAAhRwJIElS6ahZ0/rBj9Ue1u3zsBvv/0ks815Y48jXX/WlQIUoAAFKGDPAhwAseerw7o5rcCUKfPw+utxcue+5U2sUwcoXToKf/yxTwYQ3pIvHOclcGCqyWWqcw8enCJfMs5AzUYxdytaNAgvvthTZm74WHR3mbojbdQoHzz/fHeUKBFi7umZngIUoAAFKEABClCAAhSwAwE1E/nEiVOIiLC+MipweuXK7jh69Kj1hbEEClCAAhSgAAUoIAKMAcJuQIECFrh+PQanT59G/frWn7hHjxhZ7mqGDCBcQNeupsURufOs/fqloHv3/Vi58g+0a/fgnYfzfN+nTxsZgNmBt95SjySTB1/U4MeECV5yZ1dtiYHC2R95IvMgBShAAQpQgAIUcFKBtLQ0/P33QVk6aT+OHTsuy3mmonjxYqhbtyYefLAmQkMDnbTlztWsK1euyPcRN5O/C+TX+oiIRBw+fBhVq1bNLymPU4ACFKAABShAgXwFOAMkXyImoIC2Ajt2HJHA5BmafEFQgyj79x/H229LUBALNzUT5IMP4mS93lmG5bTMKUbFnpg1awySkh7A00/74ezZ/HOfPw8ZrPFDdPR9+OqrN8XBimkw+Z+OKShAAQpQgAIUoAAF7EwgOTkFkyYtkoGO7pg+/S35DDoHzZv/LjfjbELFit9h48aJePTRvoZZzseOyYdHbnYtkJycBC8v7aro45Mm3y/MX6JXuxqwJApQgAK2FVAz5y5evGiY7XZefiRR77lRgAK2E+AMENvZsmQK5Cpw4ICaHm75gEXWQnftgswkSUexYln3mv+6bFmgVKk4/PPPYRmcCTerAJ1Oh88+G4E1a/5Gly5T5YtsIjp1ipNnIPDWTXsq1oeq67Jlfti+3QvvvDMUbdrcb9Z5mJgCFKAABShAAQpQwPEF9u07KXHw3pIBjkj8+GMSgoJytqljxwSMHQusXfsXOnb8V5Zr7YqBA9vzxpmcVNn26HSxEp/vkWz7CuJNoHzoj4zU7se769c9Ua9ecEFUneegAAUoUGACatbjb7/9iilTpsgst0OGv2nqhlA1+KHiHoWFVcKwYcPw+OMtodfz59oCuzA8kUsI8P8ol7jMbKQ9CSQnJ8PXV5svCH/+CflSqE3rmjWLxYYNO8weAMk8e6tWDeUP9QL8/PM/WLVqrcxKOYy4uJt3bvn6esuASBUZGHkMX37ZAGrQhBsFKEABClCAAhSggGsJbN9+WGZ1jMHnn0fj7rvzbru7rFXQogXQqFGM/CC0EOfPX5bPlwPzzuTCR/X6aBn8aGYQyMhQP6gVHEZgYBDi43VISQE8PKw/7549eok1WMv6glgCBShAATsR2Ldvr8Rs7Y1r167Kahhyh2gu2/bt/0iM1CFyY0AwZs/+Cg0aNMglFXdRgAKWCHAAxBI15qGAFQLBwYG4dEkNAFgWsyPrqfft024ApHbtDMycuVeK75r1FGa9dpdvqi1b3mt4mJWRiSlAAQpQgAIUoAAFnFrg6tUo+fHnTcyfH40qVUxvqr8/5DNqvOT9BQsWVECPHo+bntmFUsbGNpRBj4Id+MjK26DBvbJ02WqZ2ZN1r/mvr10DYmK8JaZICfMzMwcFKEABOxRYvHgRxowZjRs3buRbu5iYGPk3MAadO3fCa6+NkNmPg/LNwwQUoED+AowBkr8RU1BAU4G6dStj714/TcpUXxBCQjQpCkWKQP4gx2pTGEuhAAUoQAEKUIACFKBAFoEXX5yIl1+OMWvwIzO7Wgnk00/j8eGHX+HCBfkAzM3uBHr3fkliA1q/bNX8+Z4SL7Cf3bWPFaIABShgicD//rcKo0ePMmnwI2v5UVFREqv1fRn4X5B1N19TgAIWCnAAxEI4ZqOApQI1a1bAnj1AogZx/ZKStClHtSUhARK8UIM565bCMB8FKEABClCAAhSggFMKHDp0BmfOHJKlWy2fAa1u+hk8OFZiz33jlEaO3qh69eohNbUyfvnFzeKmnD0LLF0ajGee6WNxGcxIAQpQwF4ELl++jOHDh8vyhFEWVUktlTVu3FicPn3aovzMRAEK/CfAAZD/LPiKAgUi4OnpIQHAH8T331v+5UBVVGJkyR9SPQ4e1KbaBw4A1apV1aYwlkIBClCAAhSgAAUoQIFbArNnf4c+fWIl4Kt1JB07pstn6PXyQ7vlAynW1YC5jQmoQL4ffzwHb74ZAjWQYe6mbg4bNCgQ48d/ChVThBsFKEABRxd47bVXZeZHpFXNiIyMlDhYz1tVBjNTgAIAB0DYCyhQCALDhnXD1Kn+8sfQ8pPPnatHxYpVsHmzl+WFZMm5ZYsvGjbMJxpllvR8SQEKUIACFKAABShAAVMENm3aiWbNrI/K7esLhIe7yQ1AZ0w5LdMUsECZMmUxadJ8dOkSAnVzlanb9etAt26BaN36JeknzU3NxnQUoAAF7FZADVxs2rRJblyVO1et3PZJ8Ndz585ZWQqzU8C1BTgA4trXn60vJIEyZYrilVf6YOhQX7mDzfxKqCW0vvoqCHPnjsGaNZ5ITja/jKw54uKADRv0aN68ftbdfE0BClCAAhSgAAUoQAGrBNSPPzExsQjS6Kb+2rUTsGvXUavqxMy2E3jggUYSr+U7CdyrBkO8ICu4GN1SUoBvv3VDq1ZFZNmrDzFgwItG0/IABShAAUcSWLv2F1n2XNYZ12BTS2GpWCLcKEABywU4AGK5HXNSwCqBnj1b4u67W6BfP1/5Umh6UZs3Q75QBGPx4vdQqlQo2rVrihkzPE0vIJeUn37qhe7dW0Etz8WNAhSgAAUoQAEKUIACWgkkJibD19fKta+yVCYkJFmWgZW7d7jZrUCdOnXw449bZcmzYXKDVTH57hKCL790x2+/qZuugEWLgNdeC8SDD4Zg69bOWLHiT3To8LTdtocVowAFKGCuwMaNG2UARIPAr3LitLQ0qPK4UYAClgvoLc/KnBSggLUCEyYMksGLUnj88QV4/fUYtGwJ6HS5l3rpEvDJJz4SQL24fEkYi0qVShsSjhnTV748bMGjj15F9eq5581r744dwNq1ITI985m8kvEYBVxKICMjA5cuRSIlJQ3BwX4ICJA1N7hRgAIUoAAFKGC2gLrBJinJ7GxGMyQkuKNYMetu/jFaOA9oJuDj44OXXholM95fwz//bMPu3dslQPq/shxMGooXv0sGRhpizJiG8hkrQLNzsiAKUIAC9iJw9Ki2MxVPnjxhL01jPSjgkAIcAHHIy8ZKO5PAwIHtDV8A3nlnFt59dxcaN06XmSFx8sUA8uMrcOSIHtu2+eLMGS+ZFv6UBBdsLYMk/42S+Pp6Y86c8ejRY6QsixWNiAjTdfbuBZ5/PghLl77N2R+mszGlkwqoO1S//Xa93JW4EqdOXUbJkm7y/4UbIiMz5O4dHRo1qo3+/TujTp1KTirAZlGAAhSgAAW0F9DrdTIT4OYgiJcGoesOHPBDkyZ3aV9RlmgTAb1ej/vuu9/wsMkJWCgFKEABOxTIyLA+9kfWZqWnWx9HK2t5fE0BVxPgAIirXXG21y4FKlYshS++GG2Yzv/XXwewc+d+HD58FeoLQ5UqlTF2bLgMioTJl8fclw9Qx+bNexe9e7+Jvn2j5TkV7nkscCczKOV8HliyJEge78igSTm7dGGlKFBQAj/8sEVmYX0ig5GJeP/9RISFQf5/++/s6s7V337bgBEjdiAwMAyffTYSJUqE/JeAryhAAQpQgAIUMCpQp05lmQWwVW4mMJrEpAMqluzOnUDt2vKHmhsFKEABClDATgXKlCkrf/f+0ax2JUuW1KwsFkQBVxTgAIgrXnW22W4FgoL80KJFA8PD3ErWrVtZlrH6EqNHT8NDD23F008nynMqqlWD3MUOw9ID+/erdXc9ZMaHt5zjQfz55wBZk9nb3FMxPQWcRkAtdTVy5KfYt28Dli+PQ+mbK8vlaJ+6Y1UtUdeyZQy+/34vmjUbiLlz30b9+lVzpOUOClCAAhSgAAWyC/Tq1QGzZu2XAZDY7AfMfKeWQK9btxr8/Pj51Uw6JqcABShAgQIUaNy4MdasWS2resiyHlZu6kZYVR43ClDAcoEs97daXghzUsBZBZKTf7LpPEO93t0wq0P9CJuaqt0UyatXo2QZn1/w11/bsX//aQmalQ4PD53ECCmPBx64B127PoaQEK636wz91lZ9yBlsTGnDmDGf48KFX2RpuYQ8Z03dWdaRI0CvXoH45puPZJCx/J2HHeY9+4/DXCq7rSj7kN1eGoeoGPuPQ1wmTSqpPus2atQXH310XmZvWFakmsHcpo2/xMR7D/XqVZGZ0rb5HG1Z7Qo/V2JiNfk+EQih5maiAPuQiVBMlqsA+0+uLNx5S+Ds2TNo2rSJLKccabVJSEgIfvhhtazcIXe3OvAWGhrC36Ad+Po5etXZ+Rz9CrL+NhVw1AEQm6KwcLsS4Advyy/H+vW7MX78eKxYESdxdcwvR81oHjOmLNavnyE/wlhQgPmn1DwH+4/mpC5XIPuQy11yTRvM/qMpp90XtmvXMYlnNwKrVsXKDA7zqzt5sidiY5vhww+HGTKz/2Q35ABIdg9T3rEPmaLENMYE2H+MyXB/pkDr1i3lptS/Mt9a/Fy9enVZ7eNPi/PbS0YOgNjLlXDNeuQRJcA1QdhqClCAAhRwfoF0WUT8lVc+wpQplg1+KKEGDYB7770qS3r8z/nB2EIKUIACFKCAlQJ16lSSWHXd0aOHnwxkmFfYnDl6+fGnnNy4MNC8jExNAQpQgAIUKCSBSZM+RnBwsFVnDwoKlpmPn1pVBjNTgAIAB0DYCyhAAQpQwOUEfvttlyxdlWAIdm5N44cMScSXXy6X5Sa43oQ1jsxLAQpQgAKuIdC//5Po1q2PxNTyx5Yt+bf5+nXIrBFfmW1ZXWJwTYK3twS240YBClCAAhRwAIGIiAgMGfI8AgIsW37c398f3bt3l2Uf6zlAa1lFCti3AAdA7Pv6sHYUoAAFKGADgWXL1uDpp828/TSXepQoAZQpk4R//z2Vy1HuogAFKEABClDgToHevVtLDK0pEn+rCtq1C8CSJW44dgyQyZmGTS2X/vvvwMsv+6JVq2AZLBmAZcs+YODzOyFvvdfpYhEX10Bi/ln2A5uRYrmbAhSgAAU0EHjppeESg7UbAgMDzSpNDZq0bt0a48aNNysfE1OAArkLMAZI7i7cSwGDAGOAsCPYuwDXnrXsCjVs2Etif1ySD6KW5c+a66OPPFCp0jBZ0uOxrLsd4jX7j0NcJruuJPuQXV8eu68c+4/dXyKbV/DAgdNYuvRn/PPPXpw8eQVu8u00IMAbdeuGo1mzxhL0/D6jcbbYf25eHhX7Qw1+ZGS4MQC6mT2WfchMMCbPJsD+k42Db/IRWLZsGUaNGomYmBgkJycbTa3X6w0zRt5440307NlL/i46z8+2jAFi9LLzQAEI6AvgHDwFBShAAQpQwK4E4uISNBn8UI0KC0vBiRMn7ap9rAwFKEABClDAEQSqVSuPsWOfc4Sq2nUdOfhh15eHlaMABSiATp064ZFHHpH4kV9i3ry5hkGQtLQ0pKamQqfTyWC/hzy7yzKRz2DgwEEoXrw41ShAAQ0FOACiISaLogAFKEABxxDQ8kYa+bwqd14yBohjXHnWkgIUoAAFKEABClCAAhSgQMELFClSBK+9NsLwuHTpEg4dOiBLGMbBx8cH4eHVULJkSaea8VHwwjwjBYwLcADEuA2PUIACFKCAkwrodHokJQFeXtY38Px5d/mwKsFAuFGAAhSgAAUoQAEKUIACFKAABfIRKCHBJMuUKWUY8MjIyJCZILcCYeWTj4cpQAHLBBgE3TI35qIABShAAQcWqFHjLglcrk0Ddu/2Q/364doUxlIoQAEKUIACFKAABShAAQpQgAIUoAAFNBPgAIhmlCyIAhSgAAUcRaBly0ewcqW31dWVGcvYudMNdepUsrosFkABClCAAhSgAAUoQAEKUIACFKAABSigrQAHQLT1ZGkUoAAFKOAAAp06NcFPP3kiOtq6yi5ZokOHDo/C09PDuoKYmwIUoAAFKEABClCAAhSgAAUoQAEKUEBzAQ6AaE7KAilAAQpQwN4FvL098eKL3fHGG74WV/X8eWD27AAMH97N4jKYkQIUoAAFKEABCti7gArSe/z4ccNDveZGAQpQgAIUoAAFHEmAQdAd6WqxrhSgAAUooJlAv35P4Icf1mPevMPo1SvVrHJjYoB+/fzw/vsvIiQkwKy8TEwBClCAAhSgAAXsXeDs2TPyGWk6fv55lQTpTUDZsjpDlc+dS0dGhjcee6wtevceLPvL2XtTWD8KUIACFKAABVxcgAMgLt4B2HwKUIACrirg5uaGr79+B+3bv4JLl87ITI5k6E34qyg3QGLgQD8MGNAHrVrd56p8bDcFKEABClCAAk4okJSUhIkT38K6dUvkZo8Y/PhjGvz9szc0Nhb4/vsv0b37EjzySGeMGDEeXl5e2RPxHQUoQAEKUIACFLATAS6BZScXgtWgAAUoQIGCF/D395FZIB8jNfVxtGzpjzVrIK9zr8fFi8AHH3iiZ89QmfkxVmaNtM49IfdSgAIUoAAFKEABBxS4du0a2rZtDG/veTIAcgM9euQc/FDNUgMi3bunGdL4+c035FF5uVGAAhSgAAUoQAF7FHCzx0qxThSwF4Hk5J8ybFkXvd5dppS7yTTyDPnRNd2Wp2LZTirAPqTdhd237ySmT1+M33/fgYgIN4SHJ0tw83RcveqBPXt0SEryxrPPPikDH63g5+et3YkLsST2n0LEd5JTsw85yYUspGaw/xQSvJOclv3n5oVMTKwm3yMC5fuEdRc2ISEB7do9hKFDT8iARppZhf3vf+745JOKWLVqE3x8fMzKW5iJ2YcKU9/xz83+4/jXsLBb4Gp9KDQ0hL9BF3anc+Hzs/O58MVn0/MX4ABI/kZMUbgCrvahqSC009LScPDgGRw6dBbJySkIDQ1EnTqVUKxYcEGcvkDPwf5ToNxOeTL2Iae8rAXWKPafAqN2yhOx/9y8rFoNgIwdO1w+8yzCCy8kWdRfpk71wpUrXTF+/McW5S+MTOxDhaHuPOdk/3Gea1lYLXG1PsQBkMLqaTyvEjBhtXNCUYACFKAABVxHQKfToUaNCoaH67SaLaUABShAAQpQwFUFjhw5gs2bV0jAc8sGP5TbkCFJePzxlTh8eBCqVq3qqpRsNwUoQAEKUIACdijAGCB2eFFYJQpQwHUFkpKScfr0ZZw6dQmJicmuC8GWU4ACFKAABShAAQoUiMCcOZ9g8OAoyD0gFm8q7+DBN6DK4kYBClCAAhSgAAXsSYAzQOzparAuFKCASwpcvHgdX321CitX/i6DHvEoVerm2PTFi+kSg8IHrVs3Rr9+7VGmTFGX9GGjKUABClCAAhSgAAVsJ/Drrz9j3Dgrg4hI9dq0ycB77/1su4qyZApQgAIUoAAFKGCBAAdALEBjFgpQgAJaCKSkpOKjjxbim29+QO/eiVi2LBUhIdlLjoqKwYoVKyQY5S/ypfIxjB79rAyKeGRPxHcUoAAFKEABClCAAi4noNPFIi3Nz6p2X7p0CUWLZsjnS6uKMWT2kI+oxYoBqswSJUpYXyBLoAAFKEABClCAAhoIcAksDRBZBAUoQAFzBW7ciEWLFkNx48b3+PXXWJnhkXPwQ5UZFAQZHEnHunUxSE9fjUcfHYyrV6PMPR3TU4ACFKAABShAAQo4kYAKfh4d/Yi0yLqv9KdPn0bFitbP/sikrVgxXZZzPZX5ls8UoAAFKEABClCg0AWs+7RU6NVnBShAAQo4nkBycgrat38FPXqcxhtvJMLLK/82qDvqRoxIkrWVz6Fdu5eRkGB5kMr8z8YUFKAABShAAQpQgAL2LKBmfmRkuMkNMm7ybE1NM+Cu4a8CqqwM6ypkTWOYlwIUoAAFKEABCuQQ0PCjTo6yuYMCFKAABXIRmDDhKzz44AU89VRaLkfz3vXEE+myFNZlvPnmF3kn5FEKUIACFKAABShAAQrkI1CiREmcO+eWTyrTD5896y7LX5U0PQNTUoACFKAABShAARsLcADExsAsngIUoEBWgRMnLuLHH9di+HDLZ3AMHpyMzZv/wP79XF4gqy1fU4ACFKAABShAAQqYJ1CuXDmcOZMhM0nMy5dbalWGKqt8+fK5HeY+ClCAAhSgAAUoUCgCHAApFHaelAIUcFWBmTOXoX//OKglrSzddDrIUlixmDFjqaVFMB8FKEABClCAAhSgAAXg5uaGBg3uxfr11mNs2ADUq9fAUKb1pbEEClCAAhSgAAUooI0AB0C0cWQpFKAABUwSWL36Tzz5pFULNRvO06pVhgRG38Y1lk1SZyIKUIACFKAABShAAWMCffq8jOnTg62KJaLCfqgy+vZ9xdhpuJ8CFKAABShAAQoUigAHQAqFnSelAAVcUeD69Rj4+6fBx8f61qsZJCVleeXz569ZXxhLoAAFKEABClCAAhRwWYEGDRogOLgBli2TacYWbsuX6xAYWB/33HOPhSUwGwUoQAEKUIACFLCNAAdAbOPKUilAAQrkEDh58iLCwnLstnhHWFg6jh+/YHF+ZqQABShAAQpQgAIUoIASeP/9L/Dxx0Wxc6f5Hrt2AZMnFzWUYX5u5qAABShAAQpQgAK2FeAAiG19WToFKECB2wIZsjaALLOs2abTuUnASuuX09KsQiyIAhSgAAUoQAEKUMAhBUJDQzF37v8wZEgJrFxp+s8Eq1a5S2y64oa8RYsWdci2s9IUoAAFKEABCji3gOmfbJzbga2jAAUoYHOBEiVCZMkq7U5z7pwbSpUqol2BLIkCFKAABShAAQpQwGUFqlSpghUrNspSWI3QvXsQ/v4bucYFUfE+1LEePYKwdOkD+O67jVB5uVGAAhSgAAUoQAF7FNDbY6VYJwpQgALOKFCmTFFcvAiZtQG4Wzn8rL54Hj+egcqVSzsjFdtEAQpQgAIUoAAFKFAIAsWLF8e8eSuxceNGTJs2EUOH/ovq1d1x113JhtqcPu2J/fvTUbVqdQl4/hoeeughmeGs4RTnQmgzT0kBClCAAhSggHMLcADEua+v07SuadOm+nPnzvWTD9fdpVHV5KHCSJ+X9xtlWaEvDh8+vNVpGsuGOK2A+nJ433018NtvW9CsmXXN3LIFqFWrkgykWDmSYl01mJsCFKAABShAAQpQwMkE1GfWJk2aGB4pKSk4ePAgzp07K7NBMtChQ1lERETA09PTyVrN5lCAAhSgAAUo4KwCvFXDWa+sE7WrRo0aReSD90/SpHtuNeusPCfJo4I8dPJQQRCGyiDINHnWdEtO/smmARb0enfDHVPqy0RqqkwL4Ob0Av/8cwijR4+WpQVirYoH0rOnP1544Q08+mhd9iGn7zW2ayD/DbKdrauUzD7kKlfaNu1k/7GNq6uU6ur9Jy6ugQxI6HJdospV+oC17XT1PmStn6vnZ/9x9R5gfftdrQ+FhobwN2jruw1LsFCAtw5bCMdsBSbgJoMfK+RsavBjlwwU1JWBjnLyqKzT6crJvpXyUP+ITqlcuXINeeZGAbsWaNAgHEWLRmD5cjV2Z9m2erX6p7uiLDlQ27ICmIsCFKAABShAAQpQgAIUoAAFKEABClCAAi4gwAEQF7jIjtzE8PDwblL/h+RxQR6PHTlyZFdmew4cOHAhPj6+q7xfK48lMiBSIvMYnylgzwJTp76Gjz8Owu7d5tdy/37gnXcCZU3mkYaZH+aXwBwUoAAFKEABClCAAhSgAAUoQAEKUIACFHANAcYAcY3r7LCtlBkfQ29VfqLM+rh6Z0POnj2bIPua37mf7ylgzwKhoYH45psP0Lnzaxgz5gZatTJtpbW1a90wdmwQFi58F6VKhdpzE1k3ClCAAhSgAAUoQAEKUIACFKAABWwkoJZSv3TpEm7ciISHhyfKly8vzx42OhuLpYBjC3AAxLGvn1PXvmrVqmWkgffeauRSp24sG+dyAhER5fDzz59hyJD38O23JzB0aBzq1cudYc8eyIwPX0RFlcfq1aNRrlyx3BNyLwUoQAEKUIACFKAABShAAQpQgAJOKaAGPf78809ZUWIy9uxRS0q43Y4tmp6ejiJFiqBv3+fQrVs3BAQEOKUBG0UBSwQYgMYSNeYpEAFZ/qqd/OP+vZzsssz+KCExPsq6u7v3kff3y6OIm5vbJXn+VR5fHTp0KEaeNd8YBF1zUhZ4h4D6ALN27XZ8/vkiHDt2GrVru8udG8nyISYDp097Yt8+oHTpUhg8+BmZKXJvjmWvXC1w2h18fGulAPuPlYDMDvYhdgJrBNh/rNFjXlfuP4mJ1ZCWFiAB0N0YBN2K/xVcuQ9ZwcastwTYf9gVTBFQ3/f//vsvw6DFtm1bERcXj8DAQNx///0S07Ox3AR58y7I1NT0fIs7deoUnn22F9TzjRs3jKb38fGBr68v3n33fXTq1MlouoI+wCDoBS3O82UV4ABIVg2+tisBmQEySir0jgx07JDn8fKHY4E85zaEfVH2t5NBkq3yrOnGARBNOVlYPgKJicnYu/eEDHxcli+zGYaZHrVrh8HHx8toTn7wNkrDAyYIsP+YgMQkeQqwD+XJw4P5CLD/5APEw3kKuGL/0eliER3dVFzcOfiRZ+8w7aAr9iHTZJjKFAH2H1OUXDeNmo0xd+4cTJ48GQkJ8bKaQ5ThO36miNzci+DgYMMsjbFjx6Jt2ydz3OyYmVY9b9q0CX369Mb169ez7s7ztRpoadmyJT77bDrU+Qp74wBIYV8B1z4/B0Bc+/rbdetlAGSSVHC4DICclh+DVcCD9fL4QP6Q7Nbr9R6yT80Q+VD2qWNXJAj63SowurzWbEtP/8W04AwWn/HmdEX5rVs2G5/K4joyo30LsA/Z9/Wx99qx/9j7FbL/+rEP2f81sucasv/Y89Wx/7q5Yv+JkR/Rmsil0dnFj1n230fyrqF8z7ydQN18xI0C5giw/5ij5VppVVyOzp07ywoPxxATk/9iJWqgom7duliwYAGCgoJyYO3cuRPt27dHZGRkjmP57fDz8zPUZcqUKfkltflxadt//+ja/Gw8AQWyC7DzZffgOzsSkAGQmVKd525VabXM8HhCXmf7ZCpp7pF9W+Shk8fHkma4PGu2ZWSszXY+zQpmQRSgAAUoQAEKUIACFKAABcwSiJZlT24OgMjNX2blZGIKUIACFLC9wMWLF/Hwww/jwoULULNATN3kJl9UrFgRv//+u2GJrMx8iYmJqF+/Ps6cOZO5y+xnNaiiBleaNm1qdl4tM8hAD3+D1hKUZZklwCDoZnExcQEL3B58kD8cE+Tct99n1kMGPLbJIMiP8r6N3IHRUZ41HgDJccrMU2v07Ip3rmlEx2JuCbAPsStYI8D+Y40e8yoB9iH2A2sE2H+s0WNe1+4/nLFg/f8BvIPfekNXLoH9x5Wvfu5tT0tLw5NPPonz589nW+4q99TZ96ampuLEiRN45plnsHLlytvLYamZG1euXMme2Mx3avmtQYMGSXzRfZw9aKYdkzuPAAdAnOdaOmNLbs8VlFHv3cYaKB88NskXgDbyKC+B0gOPHj0abSytuftNCURlbplZ06t1Q9WPR2psx9bnynpevnYeAfYh57mWhdES9p/CUHeuc7IPOdf1LOjWsP8UtLhznc8V+49Od/PmLLVaE787WN+fVR9SP2KrwSR6Wu/paiWw/7jaFc+/vZ988okhQLmlA9RqEEQtd7V06TJ07NgRakBl1qxZULNArN1iY2Px22/rC30WiLXtYH4KWCpQ+FFwLK058zm9gHwYNWmOn/xxuR0FSqaC+zs9DBtIAQpQgAIUoAAFKEABClCAAhSgAAUoYBcCKSkpmDbtM8TFxVlVn+joaEyYMN4wMLtnzx4ZnE21qrzMzGoWyMKFCzLf8pkCLifAARCXu+QO1eC9mbX19vaumPn6zmcZAAnO3CcBpm5kvuYzBShAAQpQgAIUoAAFKEABClCAAhSgAAVsKfDLL78gOTlZk1OowOk7duyQ2SA7oAZEtNpUedwo4KoCHABx1SvvAO2W6X5/SjUN/9q7u7t3zqPKDW8dOyxrLcbnkY6HKEABClCAAhSgAAUoQAEKUIACFKAABSigmcDatT9DDVxosanZGn/8sQkHDx7UbAaIqpdW9dOijSyDAgUtwAGQghbn+UwWkFgeSbIM1qJbGYZUqFDh9kyPzELCw8MrSponbr3/LnM/nylAAQpQgAIUoAAFKEABClCAAhSgAAUoYGsBFbtDqy09PR1btmwxO5C6VudnORRwRgEOgDjjVXWiNsnMj/HSnEh5FPX09PypUqVKlTObV6VKlTqy/NVqee8ljyuy5uKUzGN8pgAFKEABClCAAhSgAAUoQAEKUIACFKCArQVUkHEtN7X0VVhYGCTOrWbF+vj4aFYWC6KAowlwAMTRrpiL1ffAgQMXZPS7tTT7mjwayj/+h6tWrXpEZn6ckpkfaoi9mjwiZaDkyRMnTlyS19woQAEKUIACFKAABShAAQpQgAIUoAAFKFAgAhK3VtPz+Pr6oF69eggMDNSs3Fq1amtWFguigKMJcADE0a6YC9ZXlsLaIvFAqkvTP5DHAXmUkkdReeyXGSAfyrEIWRtxs7znRgEKUIACFKAABShAAQpQgAIUoAAFKECBAhPQcnBBbvZFgwb3oG7denBz0+Zn24CAADz99NMF5sETUcDeBPT2ViHWhwK5CRw7duyy7B9565FbEu6jAAUoQAEKUIACFKAABShAAQpQgAIUoECBCjz6aDOsXv0D4uPjrT5vUFAQHnywMWQZeHTu3BmzZ8+CLPluVblqhkrLlq2sKoOZKeDIAtoMJTqyAOtOAQpQgAIUoAAFKPB/9u4Ezubq/+P4e1bGbBiyh2RXkUqbflQqaf0hpH1RVMqvRXtRaVVpUZGSFK3aqQilRYoIWROK7LPPGLP8z3f6T6FZ7vK9936/976+j8c0M/ee9XlO4977+Z5zEEAAAQQQQAABBBBAAAEfBHr16qVq1azjaf2/rGDFscceW1rQLbcMV82atfwq1NpG6/77Ryk2lnvg/YIks6sFCIC4evhoPAIIIIAAAggggAACCCCAAAIIIIAAAgiESqBGjRrq33+A/D0LJCkpSdddd73MObelXbFWg4wdO1bWd18uqz1duhytPn36+JKdPAiEjQABkLAZSjqCAAIIIIAAAggggAACCCCAAAIIIIAAAsEWuPPOu1S3bl2fq7XO/mjWrJkGDRq0TxknnniSRo16yOsgSI0aieYg9c565ZVJ+5THLwhEogABkEgcdfqMAAIIIIAAAggggAACCCCAAAIIIIAAArYIWKst3nzzbdWpU8fr8qzgR/369TVlyht/r/7Yu5D+/fuXlt24cWNZq00qu6zVI9a2WUOHDtW0ae/ZtjVXZXXyHAJOF4hyegNpHwKhFCgomFESyPpjY6Nl/UNXUlKiwsLiQFZF2WEqwBwK04ENUreYP0GCDuNqmENhPLhB6BrzJwjIYVxFpM2f2NhMZWScXDqiJSXW+4cwHtwgdS3S5lCQWCOmGuZPxAy11x1duXKl+vXrq23btik/P7/K/ImJiWrUqJHefvvd0u+VZdi9e7feeutNPf30U9qxY6dJan2WVFgaNCk74+Occ87VtddepwMPPLCyooL+XFpaLT6DDro6FZYJMPnKJPiOQDkC4RoAKS4uVm7ubsXERCshwZ6Dusrh46EgCPDCOwjIYVwF8yeMBzdIXWMOBQk6TKth/oTpwAapW5E0f2JispWZeWKpLMEP+yZYJM0h+9QoqUyA+VMmwffyBKzAx333jdQbb0wtDYLk5eX9K5l13od1cPpVV12l668f5vUh5dnZ2Vq+fJnS09MVFxevVq1aqWHDhqU32f6rMgc8QADEAYMQwU0gABLBg0/XqxYIpwDIli279PLLH+jTT+dp69Z0JSVFqbi4xARCotSyZSOdd14v9e59gvkHOL5qGFI4RoAX3o4ZClc2hPnjymFzVKOZQ44aDtc1hvnjuiFzVIMjaf6Urf4g+GHvFIykOWSvXPiXtmnTJs2ZM1tLlnxl7uLfZG4cjFHTpm3VseNx6tatm6w79pk/4T8P7OhhTk6OPvroQ82aNUs//7ykNBhinc3RqVMnnXbaqerVq5cJXsRFxI4gBEDsmFGU4asAARBf5cgXEQLhEADZvbtA99//kvlHd6YuvDBfZ51VaPaW/Gf4zGIQrVghTZ2aoM8/r6aRI4fo3HNP+CcBPzlagBfejh4exzeO+eP4IXJ8A5lDjh8iRzeQ+ePo4XF84yJp/hAACcx0jKQ5FBjB8CvV2rrogQdu1O+/LzcfTOeagMduNWggFRVJa9dKP/yQpJkz43XKKWdq+PD7zDkLNdnOOvymQdB6FGl/gwiABG1qUVE5AgRAykHhIQTKBNweAPnzz536739vVo8e23XDDbvNnQVlPSv/+5Yt0o03JpoXeUfomWduKb3TpfyUPOoUgUh70eQU93BpB/MnXEYydP1gDoXOPhxqZv6EwyiGrg+RNH8IgARmnkXSHAqMYPiUap3J+dxzj5vtip7RqFHpOu44mW2Eyu9fQYH06qtxmjChlkaPfknHH981Iu7eL1+DR/0RiLS/QQRA/Jkt5PVXoII/6f4WS34EwkPAzQGQ9PRsE/i4RrffvtV89/yUROtAxXvuqW62xjpKzz9/+z4DmZ9fYJZtrtPq1X+UHrRVp06quSvmYLPPZNo+6fgleAJ2vGjas6dQs2f/pAULftYvv6xSQcEe1aqVqsMOO8Qs8e6kdu2aBq9D1BRUATvmT1AbTGWOE2AOOW5IXNUg5o+rhstxjY2k+UMAJDDTL5LmUGAEw6NUK/gxYsTN2rz5TY0Zk2XOyPSsXxs2SBddVFN33z1O3bv38CwTqRDYSyDS/gYRANlr8Pkx6AIEQIJOToVuEnBzAOTSS0foqKO+N9temfW6Xl5WEOSSSxI1YMB16tOnm+bP/0VPPz1ZP/200nwoHm3ODMk3q0mKzX6o8Vq8OFZZWdVM+nN06aW9zH6o1b2sjeT+CPjzoskKdIwZM1WvvPKRjjmmUF265KhNG5lzYKSdO2X2vI3WZ58lmiXftXXXXYPNC/uO/jSVvA4U8Gf+OLA7NCkEAsyhEKCHUZXMnzAazBB0JZLmDwGQwEywSJpDgREMj1KnTn1Vn3xyhyZOzDI7IHjXJ2sHhXPPraXXXvtCzZo18y4zqSNeINL+BhEAifgpH1IAAiAh5adypwu4NQDy1Vc/m6W7I/Tmm9kVLt2tyn7bNumMM1LM8t/DzH6ni3TTTdlmeW/5S4GtF34TJ8abc0aS9dRTt6pr10OqKp7nbRLw9UXTihUbTdDqbp100k5de+1uJSdX3CCzFa5uuSXJrAQ5yizzHqb4+Cr2Uqu4KJ5xmICv88dh3aA5IRRgDoUQPwyqZv6EwSCGsAuRNH8IgARmokXSHAqMoPtL3bp1q84++xhNn77TrID3rT9z50pPPHGY3n13tnnvzUdsvilGZq5I+xtEACQy57lTeh3tlIbQDgQQsE/gmWcmm7M8fA9+WC2pW1fmjv9MsxXWPE2blm2CGuUHP6y09erJHAJXYIIgO3TzzfdqypTPrYe5HCqwbNlvOu+8m/Tww5t1222VBz+sLrRurdI5kJY2z+S7TdaWWVwIIIAAAggggAACCCCAgJsFXnrpaV11VabPwQ+r7//5j5SUtMEckP6DmyloOwIIIBDWAgRAwnp46VwkCuTl7TbnOKw12xn53/srrpDZ5qpE0R7+pWjRQubOlxyzXdbz+uKLRf43gBJsF8jOztPAgXdo3LgMde7sefHWHLjppgK1b79Kd9wx1vOMpEQAAQQQQAABBBBAAAEEHCZgnf3x3ntTzQ1e/t/cdfnluzRp0hiH9ZDmIIAAAgiUCXj4sWZZcr4jgIDTBZYs+dWc0xHl89ZXe/fPCmhYh7tZZ4J4eqWkSOPH52jYsEfN2SC5nmYjXZAE7rxzrDmsL1MdOvhW4S23FOibb+bq++9X+FYAuRBAAAEEEEAAAQQQQACBEAusW7dOTZuWmDMs/W/IscdKCxeyAsR/SUpAAAEEAiNAACQwrpSKQMgEVq/+QwcfnG9L/dYWprVrS+np3hXXtKnUv3+mxo59x7uMpA6owLZt6Zo79ztdccUen+uxDgYcMSJHjzwywecyyIgAAggggAACCCCAAAIIhFJg6dKl5qYwe943V6smc05igXJyckLZJepGAAEEEKhAgABIBTA8jIBbBYqLixUX58WSjSo6mp0t3X+/tGZNFQn3e/qiiwo1efInKioq2u8Zfg2VwOTJM0xgKs/jLc0qaudRR0kbN/6mP//cWVESHkcAAQQQQAABBBBAAAEEHCuQbd7opqYW2Na+5ORoAiC2aVIQAgggYK8AARB7PSkNgZAL1K1bU1u2mFtQbLpMPEXWB97XXitdc42UkeFZwampUsuWxVqxYqNnGUgVcIGvv15gDrb3f49ba2XQ8ccXav58tsEK+KBRAQIIIIAAAggggAACCNguUKNGgglYxNpWbk5OiapXr25beRSEAAIIIGCfAAEQ+ywpCQFHCHTqdLB++snsU2TDZQU7EhKkfv2k6dMla2/Tnj1lDln3rPBDD83TokVeLh3xrGhS+SCwatUmtWrlQ8Zyslhju3ixhxOhnPw8hAACCCCAAAIIIIAAAgiESqBNmzZatqyGLdUXmnvMcnNjlGIdiMmFAAIIIOA4AQIgjhsSGoSAfwL169dWXl6CNm/2rxwr90cfyawY+Ksc667/gQOlceOkyy6T1q+vuvz69QvMNknbqk5IiiAJFCvWppucrNf22dlZQWo31SCAAAIIIIAAAggggAAC9gm0bNlKq1aVqMCGXbAWLpTatWtnX+MoCQEEEEDAVgECILZyUhgCzhC4/PL/asIE/7bBsra+eukl6eKL9+1Thw4yB2BL110nWWkqu6zjP2JibPrEvbKKeM4jgZKSqCrHzKOCTCLrfL/q1c3yIC4EEEAAAQQQQAABBBBAwGUCMTExOvnknvrwQ3Onn5/XxImp5mZB8waZCwEEEEDAkQIEQBw5LDQKAf8ELr64p2bOTDXnb/hezvjxUufO0oEH/ruMrl2t8z2kN9/893N7P7J2bYJatGi890P8HEKB5s3r6ddf7WnAsmXVdMghbe0pjFIQQAABBBBAAAEEEEAAgSALXHnljXryyVSzg4LvFf/8s7RyZW116/b/Wyf4XhQ5EUAAAQQCJEAAJECwFItAKAWqV4/X2LG3a/DgJO3c6X1LvvtOeu016e67K857ww3Siy9W/Lz1zMKFcTr88IMrT8SzQRM45phOmjfPnj/733xTTUcd1TpobaciBBBAAAEEEEAgEgXy89sqI+PkSOw6fUYg4ALNmzfXeecN0V13JaqkxPvqsrOl66+vqSeeeEXR0fa8z/K+FeRAAAEEEKhKgL/QVQnxPAIuFTjqqDa6446h6tMnyext6nknPvhAuvFG6dVXpaSkivM1aSIlJ0u//VZ+mr9WGqSqceO65Sfg0aALXHBBL02e7NuL+70bu3y5lJBQV02b1tv7YX5GAAEEEEAAAQQQsFEgNjZTRUXJ5oNZaxvTKJ8+oLWxORSFQFgKDB78P6WnH6P776/h1XbBmZnWGZmp5nzMO83K+EPC0oZOIYAAAuEiQAAkXEaSfiBQjsA555ygp54aqSuvrKuHHorX1q3lJPr/h5YulS699K9traZNk/lwu+K0Zc8ccYS0aFHZb/t+f/LJGmYFyoB9H+S3kAo0aVJXrVq11bvv+v6n37oz6t57E/W//5nJwoUAAggggAACCCCAAAIIuFjAOgvkueemKDPzTJ17bmqFN/iVddF6PzRrlnTaabV00UWjzJmZV5Y9xXcEEEAAAYcKcDqxQweGZiFgl8DRR7fTZ589o2HDnlL37vNVp06hunSRzGpfFRZKVuBjyRKpfn2ZgIXUo4cUFeVZ7S1aSOvW/TvtrFlR2ry5sVlO3P3fT/JISAVGj/6fTjzxKrN9VYasVTzeXhMnxpq5cqhOOcVEv7gQQAABBBBAAAEEEEAAAZcLxMbG6pFHntecObPNio5hatQoU2efvUsdO0oNGsisxJLWrJF++CHGnIOZYh5rZ74/p2bNmpmVWSYiwoUAAggg4GgBAiCOHh4ah4B/AtaLscmTP9XDD7+kXr0K9M47hWbrIsk6qM1aDWL9bAU8DjpIslZ9WIemn3SSZG6C8egyrxP/tUz4p59k9lBN1Ucf3WkCKR5GUjyqjUR2CNStW1PPPnuHzj9/pJkb2R6t9Cmr9403rBf8DfTJJzeXPcR3BBBAAAEEEEAAAQQQQCAsBKyDzP/zn0Um0LHArPL4UG+//a1537yt9HwP67yQjh1P0Esv/dfcSHagYmN9X1UfFlh0AgEEEHCRAAEQFw0WTUXAG4GCgj1mS6sRJgixXNOn56pWrX9yl7e91dCh0oMPyiz7lSZNkmrW/Cd9RT9t2iSzouSvZ60bXyZNitX48anmQ/KHdOCBB1SUjcdDLHDCCYfqmWfu1YABD+jqq7PM3rWFlQa9du6UOU+mhrKymuvjj0eas19qhLgHVI8AAggggAACCCCAAAII2C9g3cR35JFHlX7ZXzolIoAAAgiEQoCQdSjUqROBAAtYKz8uvPAetW69RM8/v2/wo6KqrdUgI0fK7GEq9e8v5eVVlPKfxxcvljlTQnrrrSj17JmsxYuP0dy549SmjQ97K/1TLD8FQeC44zroyy/H65dfuqpr12Q99lic5s2TduyQsrOlDRukDz+Uhg5N1Jln1jYrha40q4QeU0pKYhBaRxUIIIAAAggggAACCCCAAAIIIIAAAgj4L8D+NP4bUkIYCxQUzAjohp7WslnrDhMrYFFYWGyb5AsvvKcFC17Rk096EMUop9YxY6Tt26X77ivnyf9/yPqQvHNnqWHDuuZMiSPNQet9dPDBDSvOwDMBEbBjDu3alWW2R/vSzJmFZhu09SooKDQrhhLNEu92Zgl4FxP8OLx02XdAOkChIRWwY/6EtANUHnIB5lDIh8DVDWD+uHr4Qt74SJg/sbGZysg42bxXsN4vhJw87BoQCXMo7AbNQR1i/jhoMFzalEibQ2lptfgM2qVzNRyazeQLh1GkDwETcGMAZMeOTHXrdrk+/TTL3K3vG411yFvPnjIBFKldu/LLGDcuWrt2nWWCJFeXn4BHgyIQaS+agoIaQZUwfyJosAPUVeZQgGAjpFjmT4QMdIC6GQnzhwBIgCbP/xcbCXMosIKRXTrzJ7LH347eR9ocIgBix6yhDF8F2ALLVznyIeBQgVde+dgccJ3vc/DD6pZ1CPr110sTJpTfyT/+kCZOTNH//jew/AQ8igACCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAEiIB4DqEbBb4K23PjWHW+/xu9hTT5U5I0L/Wm5vbX01aFCiHn54mNkmKdnveigAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIhAABkECoUiYCIRLYvbvAHF6eozp1/G9AbKzUvLm0bt0/Za1fL/Xpk6hLLrnUbJHV5Z8n+AkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCYgPmIkwsBBMJFYMOGbTrwQPuO9mnZUlq7VqXbaU2aFGcOyk7WE0/cbA497xQuZPQDAQQQQAABBBBAAAEEEEAAAQQQQAABBMJUgABImA4s3YpMgaKiYsXG2hcAsQ5DHzEiQVFRNXTxxWdr3rwzlJxcIzJx6TUCCCCAAAIIIIAAAggggAACCCCAAAIIuEqAAIirhovGIlC5QN26qdqypaTyRF48u21bgu6//xb16nW0CYLYF1jxogkkRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEfBLgDBCf2MiEgDMFlUB4oQAAQABJREFU0tJSlJERrcJCe9q3enWsTjrpcIIf9nBSCgIIIIAAAggggAACCCCAAAIIIIAAAggEUYAASBCxqQqBYAgcf/whmjXL/5pWrpTS0uooIaGa/4VRAgIIIIAAAggggAACCHgsEBubaW5sOtnj9CREAAEEEEAAAQQQKF+ALbDKd+FRBFwrcNVV52n48EU65ZRss3LD926MG5egQYP6+V4AORFAAAEEEEAAAQQQQMBrgfz8tioqSlZJSZT58jo7GRBAAAEEwlwgIyNDn346Q19+OVerVq0y/2YU6YAD6qlr167q0eMUtWzZMswF6B4C3gkQAPHOi9QIOF6gU6eWqlOntd599yf17m1OMffhWrBAWr68jp59tqsPucmCAAIIIIAAAggggAAC/ggQ/PBHj7wIIIBAeArs3LlTt956i7744gvl5eUpPz9/n47OmjVTjz8+Wo0bN9bo0U+oc+fO+zzPLwhEqgBbYEXqyNPvsBZ46qlbzD92KVq2zPtubt4sDRuWrHHj7lJsbIz3BZADAQQQQAABBBBAAAEEEEAAAQQQQMA2gZkzZ+qYY7rovffe065du/4V/LAqslaCWM/9/PPP6tu3t+6443YVFxfb1gYKQsCtAgRA3DpytBuBSgTq1EnV668/qCuvrKk5cypJuN9T5t9I9euXrMceu1Vt2x6437P8igACCCCAAAIIIIAAAggggAACCCAQTIGPPvrIbFF+hbZv314a5PCkbmubrFdfnWQ+F7rCbKfIfoqemJEmfAX8OCEgfFHoGQJlAgUFMwL6r0RsbLQ5p8Pa27dEhYX2R+XXr99i/pG8T40abdJ11+WafSDLerbv9z/+kMaPr6Z582rr+efvVMeOLfZNwG+OFQj0HHJsx2mYLQLMH1sYI7oQ5lBED7/fnWf++E0Y0QWE8/yxzgApLEzh/I8Az/BwnkMBpqN4IxCs+WN9VvD111/rgw8m6aefflBWVlbpWZ/VqyeoQ4dDzdmf/XTaaT1Ne9jh3m0T09M5tG7dOp188klKT9/lUxeTkpJ0883Dde211/qU365MaWm1+AzaLkzK8VqAyec1GRkiScDtARBrrKzlju+886UJbEwxe0Tu1OGHF6t581xFm/VfGzdW1+LFccrOTtAVV/TWxRf3VHx8XCQNsev76umLJtd3lA4ERID5ExDWiCqUORRRw217Z5k/tpNGVIHhPH8IgARnKofzHAqOYGTXEoz58+2335gtjAarRYtMs1NDujnPQapd+y93EwfRkiUyZ3+mmABJNQ0fPkrnnNMnsgfFZb33ZA5ZAbBu3f6jpUvNdh1+XLVq1dLs2XPUpEnodvogAOLHAJLVbwECIH4TUkA4C4RDAGTv8dm2LV0LF67Rb7/9WbrqpEGDNPMiqqVZIVKndCXK3mn52R0CnrxockdPaGUoBJg/oVAPrzqZQ+E1nsHuDfMn2OLhVV84zx8CIMGZq+E8h4IjGNm1BHL+WB96P/DAbfruu6l66qkMEwCp3NrsimTu8E9RXNzRJv0kVatWrfIMPOsIAU/m0IIF32vAgP6l53r402hr55GLLrrYHJD+hD/F+JWXAIhffGT2U4AAiJ+AZA9vgXALgIT3aEVm7zx50RSZMvTaEwHmjydKpKlMgDlUmQ7PVSXA/KlKiOcrEwjn+UMApLKRt++5cJ5D9ilRUpnAjh07tHLlCu3eXaDU1FQdckh71ahRIyDbWd955/Vmq6t39cQT2YqJKWtB1d/HjKlugiadNGnSB2yJVTVXyFN48jfo4osvlHX+hx1XbbN8aMWKVWZOeTGp7Kj4/8sgAGIjJkV5LcAmgV6TkQEBBBBAAAEEEEAAAQQQQAABBBBAIJwFtmzZYoIJz+u996YoOXmP2rQpVvXqJeZu/Bj98kuJUlLqmrvqr9fZZ/e2bdXFxx9/oLVr39Prr2eXblvtje/11+dr27bFeuaZR3XDDbd5k5W0DhX44YcfbGxZlFavXmXmcVsby6QoBNwhQADEHeNEKxFAAAEEEEAAAQQQQAABBBBAAAEEAixgbUE1ceILevHFR3T55Vn69NNCEwD5d6W//bZLL710i5599kE99thLOvLII/+dyItHcnJydP/9N5qAS6bXwY+yau68M9ccjD7OnAcyQM2aNSt7mO8uFMjPzzcrjnbb1vLc3BwtW7aMAIhtohTkJgFzDDIXAggggAACCCCAAAIIIIAAAggggAACkS1QWFiowYMHmrMzH9CMGbtMAKT84IelZMUXRo7M0bhxv+vWW8/TG2+86hfeu+++oTPOyFX9+r4XU726dM01GSYwM8b3QsjpCIG8vDwTCLPvY9s9e/bICrJxIRCJAvb9nxSJevQZAQQQQAABBBBAAAEEEEAAAQQQQCAsBG6//Vo1aDBXTz+dbba48qxLbdrIrNpIN6tG7tJnn033LFM5qd58c5zZUiu3nGe8e+jss0tM8OaD0vNJvMtJaicJVDfRrOLiYtuaFBcXV3pujW0FUhACLhIgAOKiwaKpCCCAAAIIIIAAAggggAACCCCAAAL2C8ya9bl+/XWG7rknV1FR3pVvBUtefjnD5B1qzgjZ5V1mk7qoqEhbt27RgQd6nfVfGaxVII0bR2njxo3/eo4H3COQkJCg+PhqtjU4MTFR7dq1s608CkLATQIEQNw0WrQVAQQQQAABBBBAAAEEEEAAAQQQQMBWAevcjxEjbtCTT2Z4Hfwoa0iDBtKQIel64okRZQ95/P2PP/4wwY8Yj9NXlbBt2z1atWplVcl43uECnTp1tK2F1mqSVq1a21YeBSHgJgECIG4aLdqKAAIIIIAAAggggAACCCCAAAIIIGCrwNy5c9W2bY6aNPGv2H79Cs2h6R/KOr/Bm6ugoEDV7LvZ35RVrIKCPd40gbQOFLjuuqGqWbOm3y2LMkuaevXqpdjYWL/LogAE3ChAAMSNo0abEUAAAQQQQAABBBBAAAEEEEAAAQRsEfj00zfUp0+G32VZQYzjjy/Sd99951VZqampZussr7JUmnjnzljzwXlqpWl40vkCXbocbc6kMUuL/LysIMqwYTf6WQrZEXCvAAEQ944dLUcAAQQQQAABBBBAAAEEEEAAAQQQ8FPgp59+UKdOfhby/9k7dcrUkiULvCqsbt262r7dOgvEq2wVJl6yJFodOnSo8HmecIeAtXLj5Zdf8WsViHX2xzXXXKtmzZq5o9O0EoEACBAACQAqRSKAAAIIIIAAAggggAACCCDgjUBMTLZyco4wH4Ame5ONtAggYINAenqGTAzClqtJkxJt2rTG67I6djxc33zjdbZ/Zfj9dykmJkUpKawA+ReOCx9o2bKlHn30MVmrhLy9atRI1AknnKDrr7/B26ykRyCsBAiAhNVw0hkEEEAAAQQQQAABBBBAAAE3CuTkHGmaHa2Skijz5cYe0GYEELAErP9/rTv3vb0uueQGjR/v/3kPL7+coAsvvNbb6knvYIH//re3nnlmrNLS0hQd7dlHuVbApG/fvnrllVc9zuNgApqGgF8Cnv1f41cVZEYAAQQQQAABBBBAAAEEEEAAgaoECH5UJcTzCARGoE6d2vrzT3vK/u23KDVu3Nrrwo4++hhlZzfXF194nfXvDKtXS599Vst88H3+34/xQ3gInH766Zo37xv17NmzdEus+Pj4f3XMCrzVqlVLrVu31uuvT9Hjjz9hVgPF/CsdDyAQaQKxkdZh+osAAggggAACCCCAAAIIIIAAAggggECZQMeOR2nhwpUynzH7fS1caN15b63o8u6yPrx+8smJOu+8bnr77V0miOJd/qwsafDgVD3yyItKSEjwLjOpXSFwwAEHaNKkydqxY4c+/vhjzZ07R6tN1Ku4uEjWc8cf31Wnnnqq2rfn/BdXDCiNDJqA92vygtY0KkIg9AIFBTMCuvg8Nja6dGlsiVkjW1hYHPoO0wLXCTCHXDdkjmow88dRw+HKxjCHXDlsjmk088cxQ+HKhoTj/MnPb2veE6Sw/VWQZmQ4zqEg0YVlNd99961eeOF8TZyY7lf/cnOlbt3S9NVXy1XeHfqeFP711/M0fPiFZjusdLVr50kOafNm6bLLUs3WVyM0YMDFnmUiVUgFIu1vUFpaLT6DDumMi+zK2QIrssef3iOAAAIIIIAAAggggAACCCCAAAIRLdCly9Favz5Va7w/u3wft0mT4nXWWef5HPywCjvuuOPNeQ/TNGRIE3P4dXWlVxKTycuTXn45VueeW0fDhj1P8GOf0eAXBBBA4C8BtsBiJiCAAAIIIIAAAggggAACCCCAAAIIRKyAtf3UAw+M1dCh5+vDDzPMuQneU6xfL7M9US3NmHGb95n3y9GxY0dNn/69WQXylDnzYYJZCVKoww/P1EEHFZoDraWNG6O1eHGK5s+PNtt2/VeffHJ76dkP+xXDrwgggAACRoDlR0wDBCoRYAusSnB4yhECkbZs1hHoYdQI5k8YDWaIusIcChF8mFTL/AmTgQxRN8Jx/rAFVnAnUzjOoeAKhmdt999/q3bufE2jR2eXBho87eX27TLnfqTqvvsml67g8DSfJ+mKiopMsGOx+VpoVqksN1lKVK9eMx122BEmKNJZ1atX96QY0jhMINL+BrEFlsMmYIQ1hwBIhA043fVOgACId16kDr5ApL1oCr5weNfI/Anv8Q1G75hDwVAO3zqYP+E7tsHoWTjOHwIgwZg5/9QRjnPon97xk68C1vmct99+nVlh8aE5kDxTdetWXdKCBTLbT9Uy+azVGmdUnYEUCBiBSPsbRACEaR9KAbbACqU+dSOAAAIIIIAAAggggAACCCCAAAIIOELA2grrwQef0fvvd9eZZ96q3r2zzcHi+apff9/mmTiJWZEhvfhiilavrme2qpqkNm3a7JuI3xBAAAEEHCFAAMQRw0AjEEAAAQQQQAABBBBAAAEEEEAAAQScIHD22b3VvXsPvfnmZF1wwYvKzU1XixbRSkgoMVtkRWnDhmK1bNlKl156o7p1O9GcGeLDoSFO6ChtQAABBCJAgC2wImCQ6aLvAmyB5bsdOYMjEGnLZoOjGjm1MH8iZ6wD1VPmUKBkI6Nc5k9kjHOgehmO84ctsAI1W8ovNxznUPk95VE7BHbv3q1169bJ+p6ammoOI29WGvSwtswqLCy2owrKiDCBSPsbxBZYETbBHdZdVoA4bEBoDgIIIIAAAggggAACCCCAAAIIIICAcwSqVau2zxZX0dHRzmkcLUEAAQQQqFSAv9iV8vAkAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuFGAFSBuHDXajAACCCCAAAIIIIAAAggg4JVAcXGxZs5cqFmzvtaiRb9o27YsxcXFmH38G+mIIzqqb98T1bhxXa/KJDECCCCAAAIIIICAswUIgDh7fGgdAggggAACCCCAAAIIIICAHwLWHv1vvvmFRo2aoE6dduv003PMwcVSvXrSnj3SqlVb9f33i9W799tmX/+WGj36RjVsmOZHjWRFAAEEEEAAAQQQcIoAARCnjATtQAABBBBAAAEEEEAAAQQQsFUgNzdfl102UiUlK/Xeezmqu98Cj7g4qWNH66tYV16ZpRkzFum00wZr5MjrdM45XW1tC4UhgAACCCCAAAIIBF+AAEjwzakRAQQQQAABBBBAAAEEEEAgwAL5+QUmiHGTevVar8svN0s9qriioqSePUt01FGZOv/8J1VUVGRWhXSrIpc9T8fEZJv6Eu0pjFIQQAABBBBAAAEE/hYgAPI3BT8ggAACCCCAAAIIIIAAAgiEi8Cdd45V164bPAp+7N3nNLP71dSpOTrzzGd12GGtdPDBDfd+2vaf8/PbmuBHsu3lUiACCCCAAAIIIICAFA0CAggggAACCCCAAAIIIIAAAuEksGDBSi1YME9Dhxb41K1ataSHHsrSNdeM8im/N5mslR8lJVEqLo4y373JSVoEEEAAAQQQQACBqgQIgFQlxPMIIIAAAggggAACCCCAAAKuEnj88Ym6445sxcT43uzjj5fi4rZo4cLVvhdCTgQQQAABBBBAAIGQChAACSk/lSOAAAIIIIAAAggggAACCNgpkJGRoxUr1uiYY/wv9eKLszRx4jT/C6IEBBBAAAEEEEAAgZAIcAZISNip1F+BZs2a1YyPj19myrE25F2/atWqZv6WSX4EEEAAAQQQQAABBBBwv4C1YuPII0tkHWru73XssdLo0dbbDi4EEEAAAQQQQAABNwqwAsSNo0abZYIfTxmGwJ5GiDMCCCCAAAIIIIAAAgi4TmDZsnVq1y7HlnanpkpZWfaUZUuDKAQBBBBAAAEEEEDAKwECIF5xkdgJAq1btz7LtONC81XkhPbQBgQQQAABBBBAAAEEEHCOQF5enhIS7DtN3DpHpITTyZ0zwLQEAQQQQAABBBDwQoAAiBdYJA29QPv27WubNx8vWC2JioqaFPoW0QIEEEAAAQQQQAABBBBwkkBKSrIyMux7q1tUFGW993BSF2kLAggggAACCCCAgIcC9r0q9LBCkiHgj8CePXueMfnrmyDI28XFxV/6UxZ5EUAAAQQQQAABBBBAIPwEDjushX7+OdGWjm3ZItWta/bB4kIAAQQQQAABBBBwpQABEFcOW2Q2umXLlv81PR9gvnaY4Mc1kalArxFAAAEEEEAAAQQQQKAyASsAsmiRVFhYWSrPnps9O0rHHXe4Z4lJhQACCCCAAAIIIOA4AQIgjhsSGlSeQKtWreqYZefPWc+Z1R/XrV27dmt56XgMAQQQQAABBBBAAAEEIlsgIaGaunU7QtOn++dgHfsxcWKSLr/8XP8KIjcCCCCAAAIIIIBAyAQIgISMnoq9FBhr0h9gvqatXr16ipd5A558x45Ms8x+nZYu/U2ZmTkBr48KEEAAAQQQQAABBBBAoGKBYcMu1KOPJis3t+I0VT3z1lsxatGinQ46qEFVSXkeAQQQQAABBBBAwKECsQ5tF81C4G+B1q1bn2dWffQ1D+wwZ4AM/vuJIPwQG1txjHDZst80duwbmj37RyUnF6txY6m4OEobNpSooCBWZ5zRVVdf3UdNm9arpKVlhylGqbK6KimApyJegDkU8VPALwDmj198ZDYCzCGmgT8CzB9/9Mhb+fxp3bqxLrusr269dYrGjMkzh5h7J7ZmjUy+ZH355c1BeZ1utS8mpuL3Ht61ntSeCJQdbG99572YJ2Kk2VuA+bO3Bj/7IsAc8kWNPAj4JkAAxDc3cgVJoEWLFgeY4MezVnXm+9B169aZYwiDd5X9g7R3jdYKj5tuetKs+PjRBDiydO+9Ulzc3ilUeqfZtGkf6pxzvjCBkB66++4rFB+/X6K9svz1hszLd2V75edHBJhDzAF/BJg//uiR1xJgDjEP/BFg/vijR97K5s8NN/TXoEG/6uab52vUqDzzetwzr2XLZPKl6JVX7ledOsE7AL289x6etZhU/gpg769gZOdn/kT2+NvRe+aQHYqUgUDFAgRAKrbhGQcIxMbGjjWBjzqmKe+bra9eD3aTTN37VLl+/RadffaNuvjinXroocIK7ySrUUMaOLBY552XpSef/Fjdu/+oDz98XLVqJe9TnnXnrPWm7a9q9q1rv4T8ikAFAsyhCmB42CMB5o9HTCSqRIA5VAkOT1UpwPypkogElQh4Nn+ef/5W3X//y+rZ82M98EC2unQpC9r+u+isLGncuHh9/HEtTZkyQoceepB1E9a/EwbokWDWFaAuuKrYvT9wxN5VQ+eIxjJ/HDEMrm4Ec8jVw0fjXSZAAMRlAxZJzW3ZsuUA80K0t+nzzri4uKtD0ffCwuK/q921K0tnnjnMBD6269hj/3640h+slSE337zb7Bv8u8n7P3322TP7rAT5a6m1tfKjRHvXVWmhPInAXgLMob0w+NFrAeaP12Rk2E+AObQfCL96JcD88YqLxPsJeDN/br/9Up1++gkaOXKs7rxzg049dbc6dizQAeaEwYICadUqacGCRH33Xay5iamnvvrqfFWrFh/U1+dWnIX3A/sNcoB/teaQ9QGkFfzAPsDYYVg88ycMBzXIXWIOBRmc6iJagABIRA+/czvfvHnzeubF6NOmhSXm+1XLli37M9StHTZstAYP3uVx8GPv9vbuXaQ1a/40wZOJZjusK/d+ip8RQAABBBBAAAEEEEAgwAIdO7bQu++O1saN2zR37mIT5Fis7dt3mrMfYtS6dUv1799ezz13qNnalrfIAR4KikcAAQQQQAABBIIqwKEDQeWmMk8FWrVqdbFJO9HT9Hule2XVqlWX7PW7Xz8WFMwoXfP+7bfLNWLE3XrrrewKt72qqiLr7rJTTknWtGlj1aRJ3dLkRPyrUuP5qgSYQ1UJ8XxlAsyfynR4zhMB5pAnSqSpSMCu+VNUVKSVK3/XkiW/KisrV9WrV1OHDk3Vvn2zfVbeVtQOHnengF3zJ9S9z89vq6KiZLMKwVqJEOrWRFb94TKHImvUnNNb5o9zxsKtLYm0OZSWVovPoN06WcOg3dzeEgaDGI5dMMuQ95iVHxlV9M06xjDBfFlvFTKttCZfrvXd7mvs2Nc1dKjvwQ+rPdahi5ddlqvx4981y++vsruJlIcAAggggAACCESUwKZNO/TMM2/o/ffnqFUrmYBHvlJT9ygvL9ac4ZBgAiIl6tbtcF133UC1bXtgRNnQWecLxMRkKzOzm2loNMEP5w8XLUQAAQQQQAABFwsQfXPx4EV6080ZIZeYIMnLxmG9WfXRLBAe1goQ667CQw7pp/nz/QuAWO3LzpbZfzhNCxe+VtrcSIv4B2KMIr1M5lCkzwD/+s/88c+P3DJbx7B/OvPAdwFf54+1X//EiZ/oiScmasiQHPXuXawE65aY/a7CQpnz16THHkvSWWedruHDL2J7o/2M3Pyrr/PHKX0uC4CUlMSw8iNEg+L2ORQiNqr9fwHmD1PBX4FIm0OsAPF3xpDfH4FofzKTF4FIELC2VGjd2vqAx//eJiWZe7yiC0wgJM//wigBAQQQQAABBBCIMAEr+DF8+NP65JMJ5itLF1xQfvDDYok1a91PP12aPj1bO3e+r379bjMHXu+JMDG6iwACCCCAAAIIIIBAZAsQAIns8af3Hghs2LBVzZqZWwhtug48MFq//77dptIoBgEEEEAAAQQQiByBJ56Yok2bZmvChFzVrOlZv6tVk9l+dLcOO2ylrrnmYc8ykQoBBBBAAAEEEEAAAQTCQoAzQMJiGOlEIAWsOw3tWP1R1karLKtMLgQQQAABBBBAAAHPBZYvX6/XXntXn36aZ1bUep6vLOX//leg/v0X6aOPvtUZZxxT9jDfEUAAAQQQQAABBIxAenq6li1bZnYtyTLbi9ZQmzZtdMABB2CDgOsFCIC4fggjtwOrV6+eaHpvfQX0atAgTRs32ve/yu+/F6thw7SAtpnCEUAAAQQQQACBcBO4996xuv/+bFWv7lvPrJtQRo/O0cCBY9Wr19HmBhcb9jf1rSnkQgABBBBAAAEEHCGQmZmpSZNe0fjx45Wbm1N6w26hOUgtJibG3HASbc5Pizdbjl6gK68cpLp16zqizTQCAW8FfLh3ytsqSI+AuwXat2+qX34ptuVwwvx8KT8/Vqmpie5GofUIIIAAAggggEAQBTZt2qH163/V8cf7V2njxlLLlnn66quf/SuI3AgggAACCCCAgMsFZsyYoaOOOkKjRj1gtmrfaM5M26ldu3YpKyurdDWI9fuWLX/qqafG6Jhjji4NlLCjicsHPUKbb99t7REKSLfDXyAuLtYcgt5MP/ywVEce6V9/P/ooSj16dPGvEHIjgAACCCCAgM8COTn5+umnNWZ5/3pzl1uuUlKSdMghB5mv5mZlQbzP5ZIxsAJffLFIp56625ZtSc88M9tsozVPJ5xwaGAbTekIIIAAAggggIBDBR566EG98MLzslaAVHXt2bPHBEZ26u6779K3336rsWOfYyVtVWg87ygBVoA4ajhojFMFBg8+3/yB92/VRnGxNG5ckq66qq9Tu0m7EEAAAQQQCFuBxYt/1aWX3mvuXrtQL744wtzV9qxZ0j9Bf/75jMaMuUudOw/U0KGPaO3aTWFr4OaOLVy4WJ06FdjShY4dpUWLlttSFoUggAACCCCAAAJuE3jxxfEeBz/27pu1MuSTTz7WnXfesffD/IyA4wVYAeL4IaKBThA46aROevbZ5po+fZl69vTtAPNx4+LUpcsxZtuFRk7oEm1AAAEEEEAgIgQKCvZo5MgJmjVrpm69NdsEO7TfAdpFxiFT5sY284Zutvr0WaCLL+5jgiF9S/c9jggkF3Ry69btql/fnobWqydt3ZphT2GUggACCCCAAAIIuEjgt99+04MPPujRyo/yupWdna0pU17XOeeca3ZJ8XOblPIq4DEEAiDACpAAoFJk+AlYh2Q+++xtuu++VLNlhvf9mzMnSm+8Udsc3DnE+8zkQAABBBBAAAGfBHbvLjABjeEqKppughvZOumk/YMf/xQbFyedfXaJZszI0s8/TzUrNkep2Fq+yeUIAesgTnMepy2XVU5MDG+D7MDMyMjRjz+u0nffLdfq1X/w/4wdqJSBAAIIIIBAAAVuvvkmsxJ6l181ZGRkmJuFrvWrDDIjEEwBVoAEU5u6XC3QsGGaXn/9IZ1//q26444M9epV9UqQEpPklVdiNXFibb3//mglJlZ3tQGNRwABBBBAwE0Cgwc/aFZfrtH113u+dVKi2fHyySfzzGqRH8zKkRd1772D3NTlsG3rwQcfpDVrfjDbYPnfxTVrpBYtGvhfUISWsHnzDr300gd6++2Zio/fY1Y3y2wnV6Jt26K0bl2JOTvvQF199QCdfPLhrKKK0DlCtxFAAAEEnCmwdetWsw3oQlsat23bNnPT0M/mHL1DbCmPQhAIpAC3PgVSl7LDTqBDh2bm0MxnzWqO9rrookRzt5tkBTn2v6wbRj//XGZJYJKWLj1WX3zxnBo1qrN/Mn5HAAEEEEAAgQAJfPjhN+YD2SXm7jTPgx9lTTELP82qzXzzb/5npXe3lz3O99AJdO7cXgsW1LClAT/+GG3OfDEHgXB5JWCtiHr66bfMdrBXKynpbX388Q6ztVymnn8+0zyepalTM81r4ywTcFymSZMeNIfWX6dff93sVR0kRgABBBBAAIHACcye/YXy8vJsqWDXrl364IP3bSmLQhAItAArQAItTPlhJ9CgQZreeedRzZmzWM8997p5k/erudMtWo0bF5ll/zJ3vsWYN3slOu64Q/TII/3NG+xWYWdAhxBAAAEEEHCyQIm5O2HEiOc0eXKOrGCGL5e1JdbDD2frnnue1UcfmYNDuEIqcOKJnXTzzbHmTbuUkOBfU954I9HsXd3dv0IiLLe1ndzAgXepbt1VJjCYZ1Y1lw9g/f/WubP1laOvvlprbgYaqqeeulPduh1WfgYeRQAB1wrs3r1by5cv18aNG00fSsw5TfXVocMhqlHDnmC1a2FoOAIOFvjqq6+Un59vWwu//vpr28qiIAQCKUAAJJC6lB22AtaZIN27dyz9Kioq0ooVG7Vp0w7zIUuUmjatV7qtQnQ0C6zCdgLQMQQQQAABRwvMm7fU/FucZ25O8K+Zhx8uZWRsKr2L/aCD2DLJP03/clevHq9zz+2ul1/+REOGmBPrfbxmzZIaNmxe+nrNxyIiLpsVULz88vt02GG/aNgwz1dUde0qvflmlvr3v99sCfuwyX9QxNnRYQTCUcD6APXFFx/WqlXLTMAjWs2a7Tbvg0tMcLSa2Q5HqlOniS677CadfnovtsELxwlAn1wtsGHDBlvb/+eff9paHoUhECgBAiCBkqXciBGwDuVs375Z6VfEdJqOIoAAAggg4GCBzz//xnzwkm1LC3v2zNPs2YtEAMQWTr8KufXWS3T88fPM1ko7TIDL+6LMeZ26++5ks13Djd5njuAcU6fOVFHRcq+CH2VcVhDyueeyNGjQSLMiZLw5M8QsreJCAAFXCmzfvl3Dh19l7h7/0WwvmWHO2NJ+qyxzS/u1bNkujR17jQmSPKYxYyaZgHNTV/aXRiMQjgLR0T4uja4Aw7oJmAsBNwhwi7obRok2IoAAAggggAACCHgssGjRMnW06YiHww4r1MKFiz2um4SBE0hKStALL9ypSy9N0u+/e1dPVpZ04YWJuu22Qaz+8ILO2vrqgQde1EMP5XiRa9+k1tmo3brt0vjxH+77BL8hgIBrBNavX2+2tDvB3FwwV6+9lqGjj94/+PFPV9q3l559NssETZeYFWAnmgOXF/3zJD8hgEBIBVq0ONjW+g88sImt5VEYAoESIAASKFnKRQABBBBAAAEEEAiJwPbtmTrgAHuqrldP5jD1HfYURil+C3Tp0laPP36X+vZNMduteHbXofXZ21lnJemiiy4zH8b18LsNkVTAu+9+pR49CpSW5l+vBw3arZdemiZrOy0uKSYmW5mZ3QwFb8eZD84XyMzM0AUXnK7RozerT5+i/VZ9VNz+bt1kzuLaqWuu6WvOCdlQcUKeQQCBoAl0NftT2nVOj7Xte9euJwSt7VSEgD8CvOLyR4+8CCCAAAIIIIAAAo4TiI2NVmGhPc2yyomNZddYezTtKcU6UHv69Gc1aVJbEwhJNj9LOfstUCgwR1VY53IOGpRkVn001oQJj+iSS3rZ04AIKuX99z81H3iak+f9vKxAYv36u7V69R9+luT+7H8FP040HYk2AaEo8+X+PtGD8Ba4++4bzDlA20pXfXjb04PNzeb3379D119/MQFQb/FIj0AABE488USzHWU1W0pOTU01K8POtaUsCkEg0AIEQAItTPkIIIAAAggggAACQRU4+ODG5nBWe6pcuVJq1eogewqjFNsEGjeuq/fff1yjRj2ouXO7mW1Z6pjzQZLN+SApOuGEZHXvXkuvv36kCYDcVXr2xKGHMoa+4C9fvlHt2vmS8995OnbM148/rv73ExH2SFRUcWmPCX5E2MC7tLsrVqww/57OMQHkPT734KSTpLp1f9Xnn3/mcxlkRAABewRSUlJlBUGss2z9vZo3P8ickcfrK38dyR8cAW5nC44ztSCAAAIIIIAAAggESeCIIzrp++9/NAe0Fvld4w8/JOiUU8whBlyOFOjU6WA988ytpW0rKNij3Nzdql49vvTLkQ12WaNKSgoVZ9O55U2bFpizWza7TIDmIhDZAi+/PEZXX53h8bZXFWkNGZKpBx98zPx7empFSXgcAQSCJPDXzSNztGOH71u81qxZ05z1MzZILaYaBPwXYAWI/4aUgAACCCCAAAIIIOAggd69/6N33qnh99YyeWbnny++iDNnIHR2UO9oSkUC8fFxqlkzieBHRUAhfjzKHNlSUvLX6ocQN4XqEUDAQ4GvvvpCPXv6v09bx47Shg1rVWDtT8iFAAIhFahbt66eeGKMrC2sfLlSUlJ04403mRXSrXzJTh4EQiJAACQk7FSKAAIIIIAAAgggECiB+vVrq337tvrwQ/9e6r70UpzOPbc7H6gHaqAo1/ECUVGx2uP7zjf79G/9+ng1btxwn8f4BQEEnCuQnp6uxMRCc16APW1s1y5Kv/zyiz2FUQoCCPgl0KtXLz3wwIPmxpGaXpVjBU2GDLmm9MurjCRGIMQC/r0rDHHjqR4BBBBAAAEEEEAAgfIEHn74BrPdRpK2bSvv2aofW22OKpg6NcUcoH1p1YlJgUCYCrRt28R8YGlP5376qbo6d25pT2GUggACARfYsmWLGja07yOjhg33aOvWLQFvNxUggIBnAgMGDNB7732g5s2bKzk5pdJMiYmJql+/viZOnKSbb76l0rQ8iYATBez718yJvaNNCCCAAAIIIIAAAhEpYK0CGTXqBp1/fqJ27vSOYONG6bLLkjVu3N3m7tfq3mUmNQJhJHDWWT309tsJfvfICkRu2hRvtsto7HdZFIAAAsERiI6OVnGx/9tflbW2uDjKnCVi9sLjQgABxwgccsghmj9/gZ577nkdccSRqlWrltLS0v7+sn5v3769HntstBYu/EknnHCCY9pOQxDwRoBD0L3RIi0CCCCAgNm/u0TfffeL3n9/pnkRtFybN2eUPla/fqoOP7ydzjrrJB13XHve4DBXEEAg5AJnnnmsCgsLzd+lp/XQQ1k6/viqm2RtmzVqVJLGjr1LRx7ZuuoMpEAgjAWs83RGjRqvYcPyzIcivnd0/PhquvTSc3ht4DshOREIukCjRo3MuR32ndvz669xuvjiZkHvBxUigEDlAjExMeasn56lX9Z7/U2b/lBWVpYSEmqoSZMmsoKhXAi4XYDwu9tHkPYHVKCgYIZ9t7yU09LY2OjSN4LWPzKFhfa9uCynKh4KU4Fgz6Gvv16qm24arQYNstWnT5YJeEjmvVHptWmTtGiR9O67yfrttxp65JH/qVu3w8JUPjy6Fez5Ex5q9GJvAbfMoTVrNpm9ih9QtWpbdcklWTr2WJml/v/0ZNcuac6cKLOsP0m1ax+kp58ebpb51/4nAT8FRMAt8ycgnXdRoZMnf6oZM17Q88/n+tTq5cul666rry+/HGf+H7TpMAHTErfOn9jYTGVknGxuHokyXz6RkskmAbfOIZu671Ex3bp1MOdp/SFz5rFfV7F5q9ulS01zE9XasPkwlfnj15QgsxGItDmUllaLz6CZ+SETYPKFjJ6K3SBAAMQNoxTZbQzWiyYrSHfXXc/rq69m6ckns9Wyii28162Tbrghyez13dXcdX1d2LzRCbfZFqz5E25u9OcfAbfNoR9+WGmCHNP0/ffLlZubaw52jdLu3SVKTU02K9cOM9te/Vdt2x74Twf5KaACbps/AcVwcOHWa4Dzz79Dhx661AQyCrxqqXVzRL9+yXrppQfVqdPBXuWtKrFb5w8BkKpGNnjPu3UOBU9IevTREWYrnGd0+eWFflU7c6Y0bdrpeuaZ1/wqx0mZmT9OGg13tiXS5hABEHfO03BpNQGQcBlJ+hEQAQIgAWGlUBsFgvGiyfrgY9iwx5WTM8/s/Zkns0LWo8u60+uOOxK0Z89RZk/RW9n2wiO14CYKxvwJbo+oLdgCbp5Df62+LDJ338Xw9ynYE+f/63Pz/AkRWciqzc8v0IABt6tx4zXmhoh81ahRdVO++UbmoNRkPf74bTrpJLNk1ObLrfOHAIjNE8GP4tw6h/zostdZ//zzT7Pq+xjNmpWu6j4eiWW9JzjnnJq69953TSC0k9dtcGoG5o9TR8Y97Yq0OUQAxD1zMxxbykZu4Tiq9AkBBBCwUeCtt2ab/X+/1ujRngc/rOqtrUJHjcoz2zws0KuvzrCxRRSFAAII+C9gHcQaFxdL8MN/SkqIAIHq1ePNYegP66CD+unUU5PNSqoYZWeX3/HFi6Vrrkk0K0CbmTxPBiT4UX7NPIoAAnYL1K9f3wRABpnX9B5EPSuo/OWX49SixYlhFfyooKs8HESBoqIiE5ibaebmHRo48BQTZDvOrFY8WSNH3mK2bZxubsLbE8TWUBUCCDhdgBUgTh8h2hdSAVaAhJSfyj0QCPRdI+np2era9XKz92+G6tTxoEHlJElPlzlQLUWzZ483ZaSWk4KHQiUQ6PkTqn5Rb/AEmEPBsw7Hmpg/7hzVP/7YrhdfnGbO/PpCiYlFZlvMErOdnLR9e5RWry4xQZJGGjz4fBMoOSKgW2C6df6wAsQ5896tcyjYgoWFherbt4fOPXe5OcTcu23wPv88Sg880ETvv/+VOUfEz4NEgt3xKupj/lQBFKCni82SookTXzD/Dj2pI4/cbc6czFCHDio9p8YKzC9bJnPmVIrmzYvXhRdepauuusGs9o0NUGv8KzbS5hArQPybL+T2T4AAiH9+5A5zAQIgYT7AYdC9QL9oevrpt5WePslsgeXdm539aceOjTN3Wfc3W2EM3P8pfg+hQKDnTwi7RtVBEmAOBQk6TKth/rh/YHfsyNSaNX+YO22LzDkBKWrVqpHZKtPDvTL97L5b5w8BED8H3sbsbp1DNhJ4XFS2+WT5oovOUvv2q8wWtzlKSKg8q4mZmPM+quuDDxro9denq169epVncOGzzJ/gD9rWrVt15ZV9zb81v+rOO7NVs2bFbcjKktm+OVHffddI48e/pSZNnHfGW6TNIQIgFc9Xngm8AFtgBd6YGhBAAAHXCrz22odmSbF/wQ+r8+efv0dTp053rQMNRwABBBBAAIF/C1hBjy5d2ur44zuobdsDgxb8+HdLeAQBBAIpkJSUpLfe+twEMq7XiSfWNOf7xWvnzn/XaN2BP3lyjHr0qGmeH6CPPvo6LIMf/+45jwRawAp+9O17koYMWWoCG5UHP6y2JCdLI0bkmEDJKnOG1anauHFDoJtI+Qgg4GABZ64DczAYTUMAAQQiRSAvb7cKCnJ1wAH+99i6Oyc2tsCcB5Kj1NRE/wukBAQqEcjMzNFPP63Vn3/uKj3foXnzejrkkOaqVs3s0cKFAAIIIIBAiARiYrLNa6ETQ1Q71SLgn4C1uuvaa29Wv36X6LXXXjQfRr+hvLxMNWwYY15vlZjXXcVm27sa5tyfM/TKK0Mcece9fwLkDpWAte3VVVedp1tv3aTTTiv2qhldu8qcSfWnrriitz7++FvHboflVadIjAACXgsQAPGajAwIIIBAZAisWvWH2dfbvp0S27Qp0YoVG0rvFI0MQXoZTAHrIMR33vlSL7wwVZmZO3XooSXmDfkelZRI771XTUuWFJu7kw8yB/MO1H/+c1gwm0ZdCCCAAAIImA/dMk3w4+RSiZKSqNJ/n2BBwI0CdevW1Q033Fb6VVBQoK1bt5j5XGLO+qtrtsaqYm8sN3aYNodcYPLkCWre/Fedfrp3wY+yhltBkGOP3WxWLo3WddcNL3uY7wggEEECBEAiaLDpKgIIIOCNwO7dBape3Xx6bNOVkFCi3bv32FQaxSDwj8CqVb9r0KD7zH7AW/Xoo3nm+z/P/fXT7tIPmubPX2KWzK81+wC305gxN5fuV79/Sn5HAAEEEEAgkAIEPwKpS9nBFoiPj1fjxk2CXS31RZCAtfpj3LjHzXky5lAPP66bb85R9+7jzUqSYbLmLRcCCESWAGeARNZ401sEEEDAYwFrq6r0dPv+mdi1K1opKWx/5fEAkNAjga+/Xqo+fYaZ/X3X6/HHywt+/FVMlFnMdPTRMgdx5pi9qxeavamHaMOGrR7VQSIEEEAAAQQQQAABBBAIvsA333yjDh3yzY1L/tWdaN6Gdu++W5999pl/BZEbAQRcKWDfJ1uu7D6NRgABBBCoSODggxtqzRrflhmXV+aKFSVmCyLuECvPhsd8E7BWfgwePEJTp2aVBjc8KcUKhJx3XpEefHC7zj33JmVn53mSjTQIIIAAAggggAACCCAQZIH58+fqhBPSban1hBOytWDBTFvKohAEEHCXAAEQd40XrUUAAQSCJmAddNi0aT0tX+5/lWvXyuwLXJtDqP2npIT/F7DO/LC2vRo9OksHHug9y3HHSeefv0u33/6M95nJgQACCCCAAAIIIIAAAgEX+OWX73XIIfZUY5WzbNkiewqjFAQQcJUAARBXDReNRQABBIIrcOWV/TVhQg2/K33ppeq64orz/C6HAhAoE5g2bZ5attyqY44pe8T771deucfcBTbfrHTa5H1mciCAAAIIIIAAAggggEBABXJzc5ScbE8VSUlSbm6uPYVRCgIIuEqAAIirhovGIoAAAsEVOPPMY7R4car58r1eawXJ11+n6L//7ep7IeREYD+BF16Yqquv9m/7qmjzKmjQoBxzsOLb+5XOrwgggAACCCCAAAIIIBBqgcTEJGX5d/75312wyklM9P/mvr8L5AcEEHCNAAEQ1wwVDUUAAQSCLxAbG6MXXrhL11+fZA5E977+zEzp2muT9Nxzd7D9lfd85KhAICcnXzt3blfr1hUk8OLhXr1KNHPmfC9ykBQBBBBAAAEEEEAAAQSCIdC27VH6+Wd7alq6VGrfvrM9hVEKAgi4SoAAiKuGi8YigAACwRc47LCDNHz4EPXtm6g//vC8/i1bpP79EzV06JU68kgbPqn2vGpShrnAkiW/2rYXcA1zE1hMzB5ZQRUuBBBAAAEEEEAAAQQQcI5Aly7/0dy5NW1p0Jw5yeZ96Um2lEUhCCDgLoFYdzWX1iKAAAIIhEKgb98TlZqaYoIgj5ktg7JMYKNI1auX35KCAumNN2I0dmySHnroBvXq5cchDeVXwaMRLrB58041bLjHNoUDDojStm3pZkl8fdvKpCAEEPBfoLi42Nz1+Zt+/HGl1q1br5KSEtWvX1+dO7c2Xy0VHx/nfyWUgAACCCCAAAKOFTjGHPh3660J2r49XXXq+N7M7GyZQEq87r+/h++FkBMBBFwrQADEtUNHwxFAAIHgCpxyyhHmReN4PfzwRP3nP1+pa9cideqUo6ZNpagoacMGadGiRJMmWj17Hqs5cy5XWlpKcBtJbREhYM234mLzH5su8xmroq0DQbgQQMARAgUFe8z2i+/rpZemqVmzPSbYkau2bQvN/6fSpk3RZlvFRBMUidI553TXTTddoFq1bDod1RG9pxEIIIAAAgggUCZgvUa/+uqbdN9992rMGN8PA3nkkURdeOFV5uaJ+LKi+Y4AAhEkQAAkggabriKAAAL+ClgfMj300HW6555BmjdvqRYuXK4lSzaW3pXbpEljnX12ez32WAfVqFHB8hB/G0B+BIxA8+YNNGWKdee3f4egl2H+8UexGjSoXfYr3xFAIIQCS5f+piuuGGEC7Ts1bdrucu72NBFLZSk3V5o69SOTbq4JzA8zgfcuIWw1VSOAAAIIIIBAoATOP/9S85rgNX344U8680zrdYB315w50vz5jfTRR8O8y0hqBBAIGwECIGEzlHQEAQQQCJ5AQkI19ejRufQreLVSEwJ/CbRv31TLlhWbwNtfq4/8cdm+XUpKSlJcHC+J/HEkLwJ2CMyf/4vZZvEePftspjp2rLxE6/yeyy4r0mmnpZuAySPauvVyXXzx6ZVn4lkEEEDAgQJFRUWaOfNzffnlR2bbvx+1a1dG6euSgw8+WIcf3s2sdjuvdPs/BzadJiEQFIEos/z7+effMNsxn2R2HvhdZ5zheRBk9mzpzjvrmy2a31FsLK/3gzJgVIKAAwXY78GBg0KTEEAAAQQQQKBiAStY0bFjK339dcVpPH1m6tQ49e7NXsCeepEOgUAJbNq0wwQy7tXkyVUHP/ZuQ8OG1rlTOWbLrAmlKxP3fo6fEUAAAScLWOcavf76Kzr++LaaMeMqs6LtNY0bt8JsI7tZH3+8UUOGmE9udZ8GDDhWV13VzwR6tzq5O7QNgYAK1DEHgLz55kxNmHCobrghWTt3Vl5dZqZ0++2JeuSRNuZ1wudq1Khx5Rl4FgEEwlrAvg20w5qJzkWqQEHBDHN/ceCu2NhocwdDVOn2QYWFnt/FELgWUbLbBJhDbhsxZ7XXzfPn22+X695779bbb2eXnkHji6x1GGKPHsnmg4YJnFfjC6DJ4+Y55GOXyWajwN7z58wz/6fzz19mtrLy7aXX2rXSJZekmcDoBLZhtHGMnFzU3vPHDa+jY2MzlZFxsnndb732d7Js5LQtlHMo03w6e/XV/VSz5nKNGpVpzjKq2N2aL++/H222oa1lDnAeq5NOOqXixDwTNIFQzp+gddKBFVmBw8mTJ5gVIY+ZG6J2q1u3dHXoIKWYoydzcqSlS6WvvkrVd9/Fm9cFQ8zNFdcpJibGgT2JvNfRaWm1+AzakTMxMhrF5IuMcaaXPgoQAPERjmxBE+CFd9Cow7Iit8+fyy4boSOP/N4caFjk0/jcfHOC2rcfqGuv7eNTfjJF3hs3xtxegbK/Qd9/v0J33XW7OdPD98NNrZaNGhWvpk0vN3dKn21vQynNkQJl88f6MIwAiCOHyPGNCtUcyjZ3YJx3Xg9dcMFaDRy4x2OnLVuk/v1TNXz4WJ16Klv+eQwXoIShmj8B6o7rii0uLjaBjq/M2R6zzda435vgR7YSEmqoXbsjzfuDE0xgpLvjt7yKtDlEAMR1/5uFVYMJgITVcNIZuwUIgNgtSnl2C0Taiya7/SK9PLfPn/T0bJ188hDdc882de/u3e20zz0XZ+4Ub6V3331U0dHsCOrr/wtun0O+9pt89giUzZ9LL73XrPz42qzI8q/czZtlPlCsZz4MecW/gsjtCoGy+UMAxBXD5chGhmoODRt2mdq2/dgEawu8dtm2TTrrrNp66625bOnjtZ69GUI1f+ztBaWFUiDS5hABkFDONurmHT9zAAEEEEAAAQRcKVCzZpLZEuJx3XdfPT39dLzMGaJVXta2VzfdlFAa/JgyZRTBjyrFSIBA4AW+/XaZuVPT/3oaNJDZ5iJPO3aYjb+5EHCQQNn2Vw5qEk0JkcC8efO0YcMXGjTI++CH1eS6daWRI3fplluuDFEPqBYBBBBAAAH3CRAAcd+Y0WIEEEAAAQQQ+H+BRo3qmDM8njcHIZ5k7h5P0muvRSurnF10rG0jnnoqrvTMj0MPvaB05UdCQjUcEUAgxALWSq6kpGLFxdnTkEMPLdbixeZAEC4EHCKQn9/277M/ios5/8MhwxKyZjz77AjdfXeGz+eXWQ3v0aNE2dkrtXLlypD1g4oRQAABBBBwk0CsmxpLWxFAAAEEEEAAgf0FatSorkcfvV6//dZP48dP09lnf6s9e/J0wAHRMtsDa/PmYnO4aIp69z5VX37Z0/ycvH8R/I4AAiES2LUrS7Vr27crb506e0xAtJwoaIj6R7UIWAIcfM48sAS2mLsxtm1baw5u9t/joovSNWXKeN1772P+F0YJCCCAAAIIhLkAAZAwH2C6hwACCCCAQKQINGtWXw88MLj0Kz+/QNu3W3dYRqlevVrmEMSYSGGgnwi4SiA+Pk4Fvu0EU24/d++OllUmFwIIIOA0gYULf9Sxxxba0qzjjivRxInzbCmLQhBAAAEEEAh3AQIg4T7C9A8BBBBAAIEIFKhePV6NG5uNsrkQQMDRAg0a1NamTcXmDnn5tSVMWSdXrozTFVc0LvuV7wgggIBjBFasWKx27exZoWadebR1qzkRnQsBBFwhsGrVKn3//fzSr8zMLKWmpuqYY442X8eoRYsWHvdhx44dWr58uXJyslWjRqL5m9JOderU8Tg/CRGIVAECIJE68vQbAQQQQAABBBBAAIEQC0RHR8s6y+e337LUvLl/jdmzR1q9ukQtWzbyryByI4AAAgEQyM/PUUKCfQVHR5vIMRcCCDhaYPr0TzRy5AizMn27Obsn26x6/WfZ61tvvanExESzWr2e2c5upE4++eRy+7Jr1y69+OJ4vfLKRO3evdts8VuswsJCxcTEmi9r5Ws1XXTRheYGkEFKS0srtwweRCDSBTgEPdJnAP1HAAEEEEAAAQQQQCCEAhdeeI5efbW63y345JMonXLKUbKCKlwIIICA0wSSk2srI8OeM4+sVXPFxfytc9oY0x4EygQyMzPVr995GjJksKzVHzt37twn+GGls4IZ1uO//PKLBg26QhdeeIFZ2ZFTVkTp93feeUdduhypxx8fbc413FyaPj09vTSYkpGRXvr7n39u1pNPPqmjj+6iN96YalbVEhzdB5FfEDAC/IvJNEAAAQQQQAABBBBAAIGQCfTvf6KmT69u3tj73gTrhsqnnkrS4MH9fS+EnAgggEAABdq376QlS1JsqeG336SmTZvYUhaFIICAvQKZmRnmhoyTNWfObFmBEE+ujIwMzZz5uU477ZTS4IYVxLjttlt1443/k7Xt1d4rR8orz3p+584duvXW4brpphsJgpSHxGMRLUAAJKKHn84jgAACCCCAAAIIIBBagYSEanrgges0bFiiuaPZt7Y89lg1nX76KWrThg8EfRMkFwIIBFqgc69G7yMAAEAASURBVOfD9e230T7/ndu7fbNmxZq7vXvs/RA/I4CAAwSswMWAAQO0bt260m2qvGmSFcRYvXq1Lr304tIVH1OmvK6sLM8CKGX1WAGXt99+Ww899GDZQ3xHAAEjQACEaYAAAggggAACCCCAAAIhFTjrrOPUtu0JJgiSoKIi75oyYUKcFi5sau6UvMS7jKRGAAEEgiiQkpKqww7roi++8K9Sa3ebyZOTzYesl/pXELkRQMB2AStosXz5Mq+DH2UN2WMONJs/f77Z0uoJE/zIKnvYq+/Z2VmlZ4YsXfqzV/lIjEA4CxAACefRpW8IIIAAAggggAACCLhE4JFHhqp+/R4699wk/fpr1Y02W2CbvbVr6Msv2+rddx8xh4DGVZ2JFAgggEAIBYYOvceseKtp9v73vREvvxynww8/SQ0aNPC9EHIigIDtAtbh5KNGPeDxtlcVNcA6ByQ3N7eipz163Don5Prrh3qUlkQIRIJAbCR0kj4igAACCCCAAAIIIICAswWioqLMB4NDTEDjeF122WNq3TrXHCCabT7ok2rW/KvteXnSz+aGxvffTzB7Zcdr+PBLzKGhp8nKy4UAAgg4XaBNmzbq1ety3XXX83r44Rzzt8u7Fi9fLnNnd21zbtJo7zKSGgEEAi7w9ddf+x24sLOR69evNytkf5R1voh1EHt+fr7q1KmrQw891GwZ2kZxcdw4Yqc3ZTlbwMt/bp3dGVqHgN0CBQUzzALjwF2xsdGlb9itfSILC33c9DpwzaNkFwgwh1wwSA5uIvPHwYPjkqYxh1wyUA5tZmXzx3ptNGfOYrOP9XTz5n2lORA0t/Q1U2xsrHnjfpB69uxuVop0ZdWHQ8c2GM2qbP4Eo35P68jPb2te56eYA2k9zUG6YAmEag5Zd4kPGTLQfBD5lUaMyJH5s+bR9eOP0rXXppkAyPtq1669R3lIFDiBUM2fwPWIkv0VuPfee/T000/5W4yt+RMSEsxrpfjSIIhVcFxcvJKTk0pfU/Xp09dsPfo/1a1b19Y6KyosLa0Wn0FXhMPjARdg8gWcmArcLEAAxM2jFxlt54V3ZIxzoHrJ/AmUbOSUyxyKnLEORE+ZP4FQjZwy3TJ/CIA4d06Gcg4VmcOOHnroTs2dO9WsBEkvXelWkZQ501hPPVVDs2fX09ixU9WqVauKkvJ4EAVCOX+C2E2q8kKgZ89T9f3333uRI7RJY2JizArbmmb17YPq27dvwBtDACTgxFRQiYCH9xpUUgJPIYAAAggggAACCCCAAAIIIIAAAgh4JGB98HjHHQ+a1Wx9TQBkuHbtWqsePXLVseNucze2Ss8IWbkyyhyGnKoff4zTwIFXmm2vbmDLGo90SYRAaASsrabcdFmB2B07duiWW27SmjWrddttt7up+bQVAa8ECIB4xUViBBBAAAEEEEAAAQQQQAABBBBAwH+Bw80hR1Onfq6NGzdo3rx5mjVrngmGbDVb1lTTwQd3NOcgHa0xY46VFTDhQgABZwtUq1bd2Q2soHWZZpnZuHEvqFmzZhow4PwKUvEwAu4WYAssd48frQ+wAFtgBRiY4v0WYOm134QRXQDzJ6KH35bOM4dsYYzYQpg/ETv0tnTcLfOHLbBsGe6AFOKWORSQzlOo3wLMH78Jw66AoUOv02uvTXZtv2rXrq0vv5ynBg0aBKQPbIEVEFYK9VAg2sN0JEMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYT6B79+6qUaPGfo+659ddu3aZs4lGuafBtBQBLwQIgHiBRVIEEEAAAQQQQAABBBBAAAEEqhKIiclWTs4RKipKriopzyOAAAIIhIHAqaeepmrVqrm2JyUlJfr444+Vl5fn2j7QcAQqEiAAUpEMjyOAAAIIIIAAAggggAACCCDgg0BOzpEmV7RKSqLMlw8FkAUBBBBAwFUC1uqP887rp+rV/TsLJDo6tB/V/vTTIle501gEPBEI7f9VnrSQNAgggAACCCCAAAIIIIAAAgi4TIDgh8sGjOYigAACfgrcddfdSkur41cpDRs2VK1atfwqw9fMGRkZWrhwoa/ZyYeAYwUIgDh2aGgYAggggAACCCCAAAIIIIAAAggggAACCLhBICEhQVOnTpV1oLgvV506dfT22+9q1KgHlZKS4ksRfuUpLi7Wpk2b/CqDzAg4UYAAiBNHhTYhgAACCCCAAAIIIIAAAggggAACCCCAgKsE2rVrr2nT3lP9+g0UHx/vUduts0MaNWqkDz74SC1btizdSstaTZKamupRfjsTedpmO+ukLAQCLUAAJNDClI8AAggggAACCCCAAAIIIIAAAggggAACESHQocMhmj//e/Xp01c1a9aqMBBinRdibXfVv/8Afffd92rduvXfPpdddnnpapAmTZooOTn578cD+YPVng4dOgSyCspGICQCUSGplUoRcIlAQcGMgB5ZGBsbrago62DEEhUWFrtEhWY6SYA55KTRcF9bmD/uGzOntThYc2j9+i368cfV+vXX382/mcWqV6+uDj+8pdq2baKYmBinsdAeDwWCNX88bA7JXCbg9PmTn9/WvL5P4QB0B88rp88hB9PRNCPA/GEaeCqwa9cusy3WFM2aNUsrV67Qnj17SgMibdu21SmnnKJ+/fopKani7a4KCws1Y8Z0jRkzRuvW/aq/Dkm3Ps4tkbVllXVZddhxWVt3zZjxmVq0aGFHcfuUkZZWi8+g9xHhl2AKMPmCqU1drhMgAOK6IYu4BvPCO+KG3NYOM39s5YzIwgI5h6w3dFOmzNJzz01VtWpZJuBRoIMOyjcBD2nz5jgtXpygNWtiNHDg6RoypLe5M65GRI6BmzsdyPnjZhfa7pmA0+cPARDPxjGUqZw+h0JpQ91VCzB/qjYiReUCvswh6+bZP/74XTk5uapRI8Fsm9VYP/wfe/cBH0XRPnD8SQ9JCL0jAlKlKVUEFBVUbLyICKKoIFIUO4q9vqgoKjYEBGzYsPCqWLCgf0FABWlSQ1GaiJKQSnr+z5w5DCG5XNm7XPnt57O5u93Z2ZnvTm73dnZmVv4sw4dfZkklSLNmzTQ+7wyCTgWI4/LAWu8KRHo3emJHAAEEEEAAAQQQQMA1gZ0798u11z6sFR5/yMyZh+X440tvn6cL8iQ1VeS1196VPn0+l2nTJknfvp1KB+QzAggggAACCCCAAAJBIWB6EGnc+Lij8tKtW3epVau2xxUgpput226beFTcfEAgWAQYAyRYjiT5QAABBBBAAAEEgkDg119/k4EDb5Zbb90hTz9dVuXHv5k040LeeGO+zJuXLHfd9Yi8++7X/67kHQIIIIAAAggggAACQS5gKkVefnm2jjVS3aOcmm6vzFgkTAgEowAVIMF4VMkTAggggAACCCAQgALJyenapdVd+iPukJx2mvMZaNpUdJDIDK0wmaEDSG50fkNCIoAAAggggAACCCAQ4AIdO3bUh4JulMTE8scScZTFunXryuuvv2Ebo9ZRONYhEKgCVIAE6pEj3QgggAACCCCAQJAJ3HLLVP3xlibt2rmeMdMa5KWXMuT66x+Tw4dzXI+ALRBAAAEEEEAAAQQQCFCBm266RW644UapVs35liDR0TE6jkgj+fTTz23jiQRo1kk2AhUKUAFSIREBEEAAAQQQQAABBLwtsHHj77Jr10a59NICt3fVqpXI2WenyRtvLHI7DjZEAAEEEEAAAQQQQCAQBW699TZtFf2Bjp93vFaE6NNB5UyRkZHaZVYNGTz4Ylm+/Ecdd695OSFZjEBwCDAIenAcR3KBAAIIIIAAAggEtMCsWe/J6NEZ2vTes2yMHJkjI0Z8IGPGXORZRGwd1ALZ2bny5Zer5Oef10lS0nbJzy/QAUSry8knd5Izz+wsrVo1Dur8kzkEEEAAAQQQCE6Bzp07y6pVq+X777+XmTNnyLp1a7V19GFbZs14IfXq1dcHhs7Ra+Ux0qBBg+BEIFcIlBKgAqQUCB8RQAABBBBAAAEEfC/wf/+3Wu65p8jjHTdsKBIVdVj+/jtVatcu/8k3j3dEBAEpkJWVLY899qp8+OFirejIk27dDssFF4jog5BaZkTWrFkh48bFSXR0XXnooQnSo0fbgMwniUYAAQQQQACB0BUwFR2nn366bTYKRUVFUlhYKBEREaGLQs5DWoAKkJA+/GQeAQQQQAABBBCofAFzUzo8PFeqVLEmLZ06Fcjq1dukf/8u1kRILEEhsGrVVrn22oe1m7VU+b//y5PY2KOz1bq1SK9eBTqOTLqsX58ut912n5x22hnyyCPjuGFwNBWfEEAAAQQQQCCABEyFCJUfAXTASKrlAlSAWE5KhN4QaNmy5cUa71X6pd1NX2vrbEY33am12N9oLfaL27dv36afmRBAAAEEEEAgAAVSUjK0+yHrhqarXTtPn+ZPC0AJkuwtgeXLN8r48Q/InDnpYio6Kpo6dBBZuDBDW4F8JSNHHpBXXrmfGwcVobEeAQQQQAABBBBAAAE/FLDul6YfZo4kBb5Aw4YN41q1avWZVnx8oLPpzNtUfvyuc77OHXTZzVqLvb5169ZD9DMTAggggAACCASgQGRkhOTled79lT3r+flh2g0WTfztHqH+euDAIe3n+iGZN8+5yg+7l+kW6+GHsyUhYa089dSb9sW8IlChQEREhhQUxFcYjgAIIIAAAggggAAC3hegAsT7xuzBA4GEhIQ5uvkAnbWxR9G9WVlZ1bZu3dpS5xq67DSdd+gcq+teb9u27fH6ngkBBBBAAAEEAkygbt3qsn9/kZ7srUn41q0x0rIlg1hboxn4sdx885Ny++3p0rSp63nRHiNslSDz538smzfvdj0Ctgg5gcjINElLO1PzzU/tkDv4ZBgBBBBAAAEE/FKALrD88rCQKCPQokWLdvoyrFhjSlJS0uTi97YXrQRZol1jDddWICt0QWxBQcFIfX3QtpI/CCCAAAIIIBAwAnoulyZN6sjOnenSvLlnydbxHWXDhkI58cQmnkXE1kEhsG3bPtmzZ6sMGqQFw83JjBVy550Z8swzr8vMmfe4GQubhYpAauqZWpkbZlmFbqi4kU8EEEAAAc8EcnJy9Bp4g45jtk4OHjwocXFx0qZNWznppJOkevXqnkXO1ggEuAAVIAF+AIM5+eHh4Wbk0mSda+iNkVll5VUrRX7ULrJMl1im9cdJZYVhGQIIIIAAAgj4v8Bll10ob745U+67L9ujxC5eLNKzZwftAovLXI8gg2TjuXMXyNVXZ4hpyeHJ1L+/6HggayQzM1vi40uNnu5JxGyLAAIIIIAAAgh4ILB37x558skndOyyT23XO2lpaZKfn6/vwyQxsZqEh4eJ9pgi99xzr5xySk8P9sSmCASuAO1yA/fYBX3KtYXH6zrXqlq1asyWLVt2OshwXvG6GAdhWIUAAggggAACfixw6aVnyGefxcqBA+4nsqBA9Cn9BJkwYbj7kbBlUAn88MNq6dvX877VInRImS76aM6aNduCyofMIIAAAggggEBgCmhX8DJnzmw5/fTTdJyzeZKSkizJycm2yg+TI7M+NfWQLk+RZcuWyfDhl8nYsWMkIyMjMDNMqhHwQIAKEA/w2NQ3AqtWrbJXcByzQ239YQZFb1q8YuMxAViAAAIIIIAAAgEhEBcXq2MtXCe33hrvdtcxL70UpTepe8nJJ7cMiDyTSO8L/P13qtSta81+OnTIlLVrt1sTGbEggAACCCCAAAJuCpjKjdtvnyiPPPKwrYLDfK5oSk1NlY8//kjOPrufjlWVWlFw1iMQVAJUgATV4Qy9zGiTvkmaa9PHRX553WSFngo5RgABBBBAIDAFBg06TY47roeOtxArZiwPV6YPPojQFiQN5L//He/KZoQNcgHTcsOqKTGxULvA4qlJqzyJBwEEEEAAAQTcE3jhheflvffek/T0dJciyM3NlR07dsiQIZfYWoi4tDGBEQhgATpHDuCDF+pJ19Yfg7SW+9Zih+e0m6wtVptERnq7jtDeIXWYeH9fVusQn38IUIb84zgEaiooP4F65Pwn3daXoRdfvENbgTwjQ4cu1e6sMqVxY8e5zcoSmTy5imza1FArQJ7Uvo7jHG/AWj8SsL78lM6cqxVppbcv+TkzM0zH/4jnmq0kSqW+9375cTd7ZsyZiAhv/45wN3VsZxcw/eObybzyW8yuwquzApQfZ6UIV56Au2Vo+/bt8uyz07QrK9cqP+zpyMvLE3P7bO7c2dol1lj7Yl4RCGoBKkCC+vAGb+a08mOE5m6OzuaXxRcNGzacpOOFWJ5h+wnJ8ohLRfjPtbf9R1yplXxEwAkBypATSAQpV4DyUy4NK5wUsLIMRegj+88+O1ErM/rIsGFPS48e2VoZkiWdOonEFI/2ZW5qJyWJNuOPlg8+iJExYy6R558fqjccLXzc38m8E8xzASvLT+nUVK9eVbuGSJcaNUqvcf3zxo3xcuWVJ9hulrq+NVt4S8Cb5ceTNPvqd4QnaWTbfwU4Xv9a8M51AcqP62ZscbSAK2XonnvusXV7dXQMrn0yLUeeeOIJGTVqlERHR7u2MaERCEABKkAC8KCFepJbt259n7b8eNg46Eniy5iYmEu+++67fG+4ONOPomf7DdM8mMGpTCwV99no2b7YOjgFKEPBeVx9lSvKj6+kg3c/3itDAwZ0l/7935SFC1fIq68ulPXrd+qgjrlKGabnzXBp2bKhDBhwhvz889lSteo/rT68f94O3iNZOTnzXvmx5+eUUzrIkiX75KKL7EvcezXXaj/9FKatklrQZYR7hF7Yyvvlx5NE833kiZ5vti15w5Hj5RvzYNoL5SeYjmbl5MWdMmQGOV+xYoUlCc7OztaHiT6WwYMHWxIfkSDgzwJUgPjz0SFtRwm0aNEiRk8Qc/Ti9PLiFa8mJCSMcTRI+lERuPEhP9/FDshd3Mc/Ta1Ny48ivanj3X25mDSCB4gAZShADpSfJpPy46cHJoCS5f0yFCYXXNDTNjti4RzqSMd/13m//Ii22BgoEycu0woQ97qJsOstWSLSoUNr7QKrCtdsdpRKfvVF+XE3i6bCjO8ld/V8t50pQ+YGpKn84Hj5zj1Y9kT5CZYjWXn5cKcMLV26TEwXVlZMGRkZsmDBAhk4cJAV0REHAn4tQAWIXx8eEmcXaN68ebXw8PDP9POpOhfqReqkpKSkqfb1vCKAAAIIIIAAAgggUFqgY8fm2nVaI1m8eIuceaZ7rW3ztZ3xo48myHPPXVU6ej4jgAACCCCAAAI+E/jppx917I8My/a3du1ay+IiIgT8WYCR2fz56JA2m4CO7xEXGRn5qX4wlR8ZWvkxkMoPCgcCCCCAAAIIIICAMwIvvHCX3Hdfgvz1lzOhjw0zdWq09OnTVzp3bnnsSpYggAACCCCAAAI+Eti7d6+lezp8+LCl8REZAv4qQAWIvx4Z0mUT6Nu3b6R2c/WRfuilc0phYeHpWvmxEB4EEEAAAQQQQAABBJwROP74ejJ58q0ydGiC7N/vzBb/hDHdGL3wQrSsXt1cHnxwjPMbEhIBBBBAAAEEEPCCAAOWewGVKENCgAqQkDjMgZvJP/744wFNfT/tmzVLX8/dtm3bL4GbG1KOAAIIIIAAAgggUBkCZiyZRx65QwYNqiYffBAuhRUMvWYesLz88njZvLmzhn9Cu9GKroxks08EEEAAAQQQQOCIQMeOHSUiIuLIZ0/fNGrU2NMo2B6BgBBgDJCAOEyhmchWrVo11+6u7jS5N2N+bN269afQlCDXCCCAAAIIIIAAAp4KnHNOd+3GaqbcddfzMm3aOrnkksPSvXu+tGolEhUlti6y1qwR+fzzqrJtW6x2mzVWBwbt7elu2R4BBBBAAAEEELBEoGvXbpKYmCgpKSkex6cPGsupp5qe5pkQCH6BsODPIjkMVIGWLVs+o1/INxenf4O+6hCUjietJDnJcQjX1ubmfqGdH3hviowMF3PS0Qoeyc+v4FFE7yWDmANYgDIUwAfPD5JO+fGDgxDgSaAMBfgBrOTkV2b5+euvQ/Lhh0tk1arVkpS0W/Ly8qVmzUStIGkvZ511ivTu3d52jVbJROzegUBllh8HyZLMzK56bR+hs6NQrPMHAX8tQ/5gQxoqFqD8VGxECMcC7pQh7RZeOnRop116utCnZznJqFGjhnz22Rf6IIg+CeKDqVatGtyD9oEzuyhbgBYgZbuw1A8EtGKgRolktCvxnrcIIIAAAggggAACCLgtUKdOdRk79kLd3sxMCCCAAAIIIICA/wuEh4fL+PHXyRNPTNEK90yPEty0aTOfVX54lFA2RsACAWrfLEAkiuAVoAVI8B7bYMmZO0+NBEveyYfnApQfzw1DPQbKUKiXAM/yT/nxzC/Ut/bH8pOd3VYKCqpq6w/TwjvUj5D/598fy5D/q5FCuwDlxy7Bq7sC7pah/Px86dXrVO2uM8ndXYtp/fHll19L8+bN3Y7D1Q1pAeKqGOGtFGAQdCs1iQsBBBBAAAEEEEAAAQQQQCCkBCIiMmxdX1H5EVKHncwigAAClSIQGRkpb775ltSqVdut/ZsxRB544EGfVn64lVA2QsBCASpALMQkKgQQQAABBBBAAAEEEEAAgdAUoOVHaB53co0AAgj4WqBFixby0UcfSb169SQqKsrp3VevXl3uu+8BGTHiSqe3ISACwSBABUgwHEXygAACCCCAAAIIIIAAAggggAACCCCAAAIhIdC27YmyYsWPMmDAebYurSIiIsrNt+nyqlWr1rJw4acyatSocsOxAoFgFWAQ9GA9suQLAQQQQAABBBBAAAEEEEAAAQQQQAABBIJSIDGxmrzyyquye/cumTVrlnzzzddy4MABHYfqn4GoEhISpHPnLjJu3Djp3r2HhIUxFHRQFgQyVaEAJb9CIgKEsgCDoIfy0Q+MvLs7cFpg5I5UeluA8uNt4eCPnzIU/MfYmzmk/HhTN/jj9qfyY8YASUvrqzecIhj8PICKnj+VoQBiI6nFApQfioKnAt4sQ4WFhRIe7l+d/jAIuqclhu09EfCv/wZPcsK2CCCAAAIIIIAAAggggAACCCCAAAIIIIBACAv4W+VHCB8Ksu4nAnSB5ScHgmQg4AuB3Nw8+fXX32TXrn+aRDZqVFs6dGgmVarE+GL37AMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEPCZABUgPqNmRwhUjoDp+/G779bK9OlvyubNO6Vdu3Bp2jRX+34U7ScyWitEiqRx4wYyfvzlct553ekTsnIOE3tFAAEEEEAAAQQQQAABBBBAAAEEEEAAAYsFqACxGJToEPAngQMHDsmECY9LTk6S3Hhjpg56JbaKj3/TmG17u3ZtulaQTJEXX2wsM2bcK02a1P03CO8QQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAFKACJAAPGklGwBmB7dv3yeDBE+W22w7JoEGFDjfp1Elk5sws+frrJDn//BvkjTcelZNOOsHhNqxEAAEEEEAAAQQQQAABBBBAAAEEELBGID8/XxYtWiT/+98CWb36F8nIyNCIw6RBgwZy6qmnyhVXXCFt255ozc6IBYEQEtBOcJgQQKA8gdzcL4rKW2fF8sjIcFuXU6abqvx8x5UUruwvJSVdzjxznDz99EHp1s2VLUW7yRIZNaq6fP75C2LGCGHybwFvlSH/zjWps0qA8mOVZOjGQxkK3WNvRc4pP1Yohm4c/lR+IiIyJC2trxQVRegcusck0HLuT2Uo0OxIrwjlh1LgqUDJMpSXVyDvvvuOPPjgg9qDR7aeU9LKjL5GjZrahXkj7bljlrRp06bMMP66sFatGtyD9teDEwLpCg+BPJJFBEJOYOLEZ2TcuEMuV34YKHMOvf/+VLnuukf1Bxy/4EKu8JBhBBBAAAEEEEAAAQQQQAABBBDwiUBWVpb23jFI7rrrTvnrrwPlVn6YxKSkJMv69eu1544B8tJL032SPnaCQDAIUAESDEeRPCBQQmDt2h3y22/rZPjwghJLXXt77rlFEh39uyxevNq1DQmNAAIIIIAAAggggAACCCCAAAIIIFChQE5Ojpx33nmyfPkKhxUfpSM6dOiQPPHEFHn22WdLr+IzAgiUIUAFSBkoLEIgkAVmzHhHW39klBrs3PUcjR+fqU8UvO36hmyBAAIIIIAAAggggAACCCCAAAIIIOBQ4J577pFNmzZJbm6Ow3BlrTTdZD377DOyatWqslazDAEESghQAVICg7cIBIPAkiVrpX9/z3Nixg7ZvPk3KShwvyWJ56kgBgQQQAABBBBAAAEEEEAAAQQQQCC4BH799VeZP3++ZGdnu52x1NRUGT16lBQWWjemrNuJYUME/FiAChA/PjgkDQFXBQ4cOCQ1axbpgGyubnls+DAdnqpVqzCtBNl97EqWIIAAAggggAACCCCAgE0gLMzceOKnNcUBAQQQQMB5gccff1xMV1aeTqYS5NtvF3saDdsjENQCXKUF9eElc6Em8McfydKokXW5btiwQPbvT7YuQmJCAAEEEEAAAQQQQCCIBLKz20pqaj8pKgrTOYgyRlYQQAABBLwmYMb+WLJkiSXxmwqQF1980ZK4iASBYBWgAiRYjyz5CkkB02rDypaPhYVhOpaIRsqEAAIIIIAAAggggAACRwlERqZpd7FVqfw4SoUPCCCAAAIVCWzcuFHCw627Jbtx44aKdsl6BEJawLr/tpBmJPMI+IfA8cfXk99+s+7Rs507I6Rp0/r+kTlSgQACCCCAAAIIIIAAAggggAACCPiBQH5+vhw8eFCSk5NdHoNjy5YtkpGRYVkuTFqysrIsi4+IEAg2AQtGCgg2EvKDQOAKVKsWLzk5UZKZKRIf71k+zNjnv/1WKM2aUQHimSRbI4AAAggggAACCCCAAAIIIIBAoAv8+eefMm/ey/LllwskLS1FatUKt/XCcfBgodSvX18uuOByGTp0hCQmJjrMak5OtphKC6sm03NHbm6uxMXFWRUl8SAQVAJUgATV4SQzCIiecE+TBQs+kSuu0BoMD6avvhI5/fST6QLLA0M2RQABBBAITIH8/AJZsWKTrFy5SXbu3KFd3JgftfXk5JPbSe/e7cU8cMCEAAIIIIAAAgggEBoCpnLh6acfkYUL58lVV2XKW2/lSe3a/+bdjAG1d2+KvPPOf+Xss5+SceMmabgx5d5PqVatmsTGxlrWaqNIE0Dlx7/Hg3cIlBagC6zSInxGIMAFxoy5WF5+uYrW/rufETOOyPPPJ8j48cPcj4QtEUAAAQQQCDCB7OxcmTLlda3ouFxmznxQf7TOlf79v5Xzz/8/adRovj7tN0UrQK7WH7WPyu7dfwVY7kguAggggAACCCCAgKsCf//9t1x0UR+9Lpwj3357SMaMObryw8Rnhk5t3Fhk4sRs+eqrFFmz5r8yYsSFkp2dXebu2rfvIFWqVClznTsL47ULkOjoaHc2ZRsEQkKACpCQOMxkMpQEmjSpK//5z7kydWqM29mePTtKOnXqoXNzt+NgQwQQQAABBAJJYPXqJK3cGC2HD78vixYdkpdeypBrrimSvn1F+vQRGTJEZPLkLFm6NF0/L9FKkevkjTc+D6QsklYEEEAAAQQQQAABFwTMOB3Dhp0jN9ywTe6887BWMlS8sen96tlnM+TUU1fqteTgMscHad68ebmtQyrew7Eh+piLVSYEEChXgAqQcmlYgUDgCtx559Xy44+NtPllhMuZWLQoTN59t7Y8+ugEl7dlAwQQQAABBAJRYOnS9TJy5N0yffoBfXIvV/ttLj8XEXpqHTiwSD7/PF3mz39ZnnxyXvmBWYMAAggggAACCCAQsAIPPzxRH4LZo12NazcZLk7XX58jDRqsk9mzXzxmy/DwcBk+fLhERno+MkGNGjVkwoQbjtkHCxBA4F8BKkD+teAdAkEjEBUVKR9++KRWgBwvDz8cqwOjV5w1M/7WM89Ea8uRBvLxx09LQoJ1zTEr3jshEEAAAQQQqByBffsOapePj2hfzuly4onOp0G7bpbXXsvSbg4WyEcfLXV+Q0IigAACCCCAAAII+L3AunXrZOPGRdrllfv9iz/0UIa8+uozYrrRKj3ddNNNOq6cXlB6MJmKlA4dOkqbNm09iIVNEQh+ASpAgv8Yk8MQFTADtH7xxfNSs+YgOeusBB0XJFIOHToWIz1dtAuPCO3jPEG7/ThH+7ScIfXq1Tg2IEsQQAABBBAIQoEJEx6T++9PlyZNXM+c6QZh+vRMuffe5yU5WU+oTAgggAACCCCAAAJBITB79pNy882HROsY3J4SEkTHAjGDps89Jo7aOor6lClTtOWxg6bHx2x19IIaNWrquHWzjl7IJwQQOEZAh+lhQgCB8gRyc78oKm+dFcsjI8Nt/T4WFRVJfr7rTSqdTcP+/ckyd+7H+oTqt9oaJEsaNjT7Ffnjj0I9mcfKuef20qcaLtabP3WdjZJwfiLgqzLkJ9klGRYLUH4sBg3B6AK9DJlxP+65527tysqzyouXXorU/p2HyqRJI0KwFLif5UAvP+7nnC2tEPCH8hMZmSapqf2kqChMZytyRRy+FPCHMuTL/LIvawUoP9Z6+ltshYWFcsopLWTFihQx3Z96MiUniwwe3EQWL157VDT2MnT//ffr/Zq5kpaWdtT6ij6Yrq/eeWe+dO3ataKgfrG+Vq0a3IP2iyMRmonwvLO50HQj1wgElED9+jXl7ruvts05Obla8ZFs+5FWr151iYuLDai8kFgEEEAAAQSsEpg16z0dnNKzyg+TluHD82XAgM/kjjuusHRAS6vySTwI+KNAXl6+mDk2NlofyPHg8Vp/zBxpQgABBBAIaIFt27ZJy5bhHld+GISaNUUfRE3XOUdiYmKOcXnooYekUaPGOg7rZElJSTlmfekFVapUkTp16si8eW9Ku3btS6/mMwIIlCFABUgZKCxCIJgFYmKipWnT+sGcRfKGAAIIIICAUwIrVmyQRx5xKqjDQKb75nr1CmTXrgNy/PH1HIZlJQKhKmCepv3661+069UFsnbtDr2plC/R0WGSmVmkfaAnyOmnd5XRoy+W5s0bhCoR+UYAAQQQ8BOB337bKSec4P7YH6Wz0aRJuOzdu1fPcc1Lr7J9HjXqGunXr7/cd9+98sMPS7WHkHxJN/2VF0/mQQEzXkhUVJSMHDlKbrrp5jIrU+zheUUAgaMFqAA52oNPCCCAAAIIIIAAAiEgkJWVrTdgc/Xpc2sy26FDnu2mLhUg1ngSS3AJ/PTTZjHj7bRtmyFXXZUpnTuLVn78m0czhs6XXy6UK674Tk48saNMnXqLVK+uHaczIYAAAgggUAkCBQUF2jrRur4NTTdaJk5HUxMdkO61117XsVsPaddby3VeIXv27NHr1Qjp2LGjraurrl272T47iod1CCBwrAAVIMeasAQBBBBAAAEEEEAgyAXS0rL0STrrut2pWTNPf7BmBLka2UPAdYFp097WcXbelxdfzNQKkLK3N92DDBtWJEOHpmuXHiu0NcgYefPNR6V9+6Zlb8BSBBBAAAEEvChQp05d2b+/RE29h/v6888iW7dVzkRTvXp1Had1gG12JjxhEECgYgHrfvVVvC9CIIAAAggggAACCCDgFwJm3IHDh617su/w4QjbWAZ+kTkSgYCfCDz//Hwd9PU9+fjj8is/SiY1TIdHHTGiQGbNStaxdSbJtm37Sq7mPQIIIIAAAj4RaNeunWzYYM11Yq72pJWZGaktG6v7JO3sBAEEjhWgAuRYE5YggAACCCCAAAIIBLmA6V4nNVVEhyWwZNq8OUafbm9iSVxEgkAwCKxZs11bc8yXuXOzXO5qTu87yVNPpco11zxYYZchwWBFHhBAAAEE/Evgn4HGG8vGjZ6n64svRHr1Ot3ziIgBAQTcFqACxG06NkQAAQQQQAABBBAIZIE2bY6T9es9z4GOUymbNhVRAeI5JTEEkcBNN02Rp5/OcLnyw07Qs6dIp04HZM6chfZFfveamnqm36WJBCGAAAIIWCMwcuREmTmzqkeRFWkjklmzqsuoUTd7FA8bI4CAZwJUgHjmx9YIIIAAAggggAACASowYsTF8vrr8R6nftEikTPO6CKRkTrCJRMCCMjKlVt0jJ0UrcDwDOPGG7P1xtH7UmTuIPnRFBmZpt2ZdNcUhWvawnT2o8SRFAQQQAABSwTOO+987YqxsSxf7n50b74ZKU2a9NKHZE50PxK2RAABjwWoAPGYkAgQQAABBBBAAAEEAlHgggtO0Ru1cZKU5H7qTb/OTz9dVSZMGO5+JGyJQJAJvPPOZzqGR7rHuapfX6Rx4xxtqbXT47i8EQGVH95QJU4EEEDAPwQiIiJk2rTX5bbbasru3a6n6ZdfRF56qbZMnvyC6xuzBQIIWCpABYilnESGAAIIIIAAAgggECgCpsXG88/fKTfdlCA5Oe6lesqUGDn//LPp/so9PrYKUoGVKzdIt27WZK5r18OyatVWayIjFgQQQAABBFwQaNGihUyd+oYMHVpDfv7Z+Q0//TRcH46pJ6+++gmDnzvPRkgEvCZABYjXaIkYAQQQQAABBBBAwN8FTj21nQwcOEj7Zo6T7GzXUjt9erQOjtlc7rprpGsbEhqBIBf4++90qVfPmky2bJmnrbS2WxMZsSCAAAIIIOCiQM+ep+p4VJ/Jvfe2lDvuiJedDholrlsncs01iTJvXnd5//1vxVSgMCGAQOULRFZ+EkgBAggggAACCCCAAAKVJ3DbbZdLdHSUnHfefHnqqQw5+WTHafnrL7H9AI6ObivvvXe/REVxSe1YjLWhJhAWZl2OI/Xfq7CwwLoIiQkBBBBAAAEXBdq0aSMLFy6TDz6YL+PHPyMFBck6zlWBdtOYpeeoMK0UiZO1a8O08r+ZjB59h/Tvf7aEWXkydDG9BEcAgaMF+LV2tAefEEAAAQQQQAABBEJQ4IYbLpW+fbvJzTdP0QqNg3LZZenStavowJWiP2BFkpNFVq8W+eSTePnpp2i5774xMmTIGSEoRZYRqFjAVAoePixSpUrFYSsKsX9/mNSuXaeiYKxHAAEEEEDAqwKRWiM/dOhw25ysF4Zr166V/fv32yo6evQ4Tsf66ChVq1b1ahqIHAEE3BOgAsQ9N7ZCAAEEEEAAAQQQCDKBDh2ayTffzNAftDu0ZccieffdX3XQy4O2XFarFiedO7eWQYP6ysyZXcUMjMmEAAJlC5j/pQ0bDtoqEcsO4fzSdeviZdiwts5vQEgEEEAAAQS8LFCzZk054wwehPEyM9EjYJkAFSCWURIRAggggAACCCCAQDAIdOrUXLs1GB8MWSEPCFSKQP/+p2tXIb9qBYiLA+uUSm1ensgPP4TJ00+3KbWGjwgggAACCCCAAAIIOCfAIOjOOREKAQQQQAABBBBAAAEEEEDACYFLLjldPv88WtLTnQjsIMjHH4fJWWd1l/j4WAehWIUAAggggAACCCCAQPkCVICUb8MaBBBAAAEEEEAAAQQQQAABFwViY6Pl+uuHyUMPuT8ISEqKyNSpCXLHHSNd3DvBEUAAAQQQQAABBBD4V4AusP614B0CCCCAAAIIIIAAAggggIAFAuPG/UfOP/87mT9/u1x6aYFLMebmiowfHyeTJo2WRo1qu7QtgRFAAAEEEECgYoEdO3boOfpdWbp0qeza9bsUFRVJrVq1pUePHnLRRQPl1FNPtQ3wXnFMhEDA/wWoAPH/Y0QKEUAAAQQQQAABBBBAAIGAEggPD5d33nlMLrzwFjl48A8ZOzZPdFGF04EDoq1H4uXMMwfKFVecU2F4AiCAAAIIIICA8wLbt2+X664bL9u3b5NDhw7ZKj7sW+/bt0/Wr18n77//niQkJGhLzKelf//+9tW8IhCwAk5cggZs3kg4AggggAACCCCAAAIIIIBAJQlUqxavY4E8p0+W9pFBgxJ0QHPRGy1lJyYjQ2Tu3HAZOLCaXHPNDXL77VeWHZClCCCAAAIIIOCWwNy5c+Scc/rLypU/S4r2NWlafZQ1mYqRPXv26MML18q1146WvLy8soKxDIGAEQgLmJSSUAQqQSA394uyzwYWpSUyMtzWpNCcdPLzCy2KlWhCSYAyFEpH2/q8Un6sNw21GClDoXbErc0v5cdaT3+PbcWKjfLcc/Nk48YkOflkkdatMyU2tlBbh0Tr06axsm1bmAwZ0k+fSh0idepUrzA7lVl+IiPTJDW1n944Ciu3QqfCDBCg0gUqswxVeuZJgMcClB+PCUM+Al+XoenTX5Qnn3xC0tLSXLKvUqWKrVus+fPfl4iICJe2LRm4Vq0a3IMuCcJ7nwpQ+HzKzc4CTYAKkEA7YsGVXlMxtmzZBvngg0WyatUmSU5Ot1WYxcfHykkntdR+tc/SbiV6SGRkpO3JDSrRguv4+yI3vr7o9kWe2IdvBShDvvUOtr1RfoLtiDqXn/T0LFm3bods3rxbsrNzpWbNRL2uOUFatWrk0o2Vyiw/VIA4d6z9PVRlliF/tyF9FQtQfio2IoRjAV+WoVWrVulDBpdo5f0hx4kqZ218fLxMmHCD3HHHpHJCVLyYCpCKjQjhPQEqQLxnS8xBIEAFSBAcxADNwvLlG+WWW56Q447L1IFD06VLF5G6df/JTGqqyNq1IgsWxMtPP0XLww9fL4MHn0YrogA91pWZbF9edFdmPtm39wQoQ96zDYWYKT+hcJS9l8fKLD9UgHjvuPoy5sosQ77MJ/vyjgDlxzuuoRJrqv6oz8vLkapVq4qpXPDmw4wFBQV6P6Gz7N69yyPeqKhoGTZsmPTr11+6desm9erVcyk+KkBc4iKwxQJUgFgMSnTBJUAFSHAdz0DIjWn18fDDs+XrrxfJs89m6NOQjlNtBgqdODFeqlXrKDNm3CUxMdGON2AtAiUE+OFWAoO3bglQhtxiY6NiAcoPRcETgcosP1SAeHLk/GfbyixD/qNAStwVoPy4Kxea25lKiC+//FKmT39BtmzZYuvBISzMdKNYJFFRUbYKhRtuuEkrKrrYen2wUmnRoi9k3LixLnd9VV4a4uMTJDo6Stq2bSsPPfSIdO7cubygRy2nAuQoDj74WIAKEB+Ds7vAEqACJLCOVzCk9q67XpB9+77Ryo/D2rWVcznSayaZNi1aVq9uI/PnP6bbud8vp3N7JFSwCPDDLViOZOXlgzJUefbBsGfKTzAcxcrLQ2WVH3vlh8k5Y4BU3vG3Ys+VVYasSDtxVL4A5afyj0GgpGDlypUyevQo7X4q1WElRPXq1aV58+Yyd+4r2hNEE8uyd+GF52vX2sssi69kRCbNF188WB599DFbRU7JdaXfUwFSWoTPvhQI9+XO2BcCCCCAQPkCn3yyTLu2+lYHCHW+8sPEpg+OaHdZuXqRtFWeeebt8nfAGgQQQCCABfLzC3Tw5N/lxx836XflDjl8OCeAc0PSEUAgEAWys9seGfy8sJAB0APxGJJmBBBAwJcCzz33rHZpfYl2P7XbYeWHSdOhQ4f0ocbVcsYZZ8i33y62LJmmxYm3JpPmt99+S7vkvli79Mrz1m6IFwGPBWgB4jEhEQSzAC1Agvno+lfesrKypUePq3XA80PSsKF7aTt8WOTcc6vK++8/L82a1XcvErYKKQGeXAupw+2VzHq7DOXm5smHHy6VJ5+cL3v3/i3h4eFSWFior2H6WiTVqyfIddddKFdffY4kJsZ7JY9E6j0Bb5cf76WcmP1BoDLKj6kAyc9P1JYf/iBAGjwVqIwy5Gma2d5/BCg//nMs/DUlL788S1tGTK6w4qOs9JuWFe+88652jdW9rNVOLzOtTrp27SzJyclOb+NOwCpVqmhLkIv1Yc4Xyt2cFiDl0rDCBwK0APEBMrtAAAEEKhKYP/9bOe+8bLcrP0z8es2hNwIzZObM9yraHesRQAABvxf4+ectcuKJ18j11z8n69fv1B9u6fL336nFr2m21x07/pD77ntV+yAepRXIS/w+TyQQAQQQQAABBBBAIPgFkpKS5PHHH3er8sPomJYVI0deLZmZmR5hHdanJM1YI96ezH4WLlyoXW394O1dET8CbglQAeIWGxshgAAC1gq88cb/ZMSIbI8jHTiwSD75ZIltMDWPIyMCBBBAoJIE5sz5XM4//x7ZteuApKdr8zYH0+HDufLXX6kyduwzcuONL/D958CKVQgggAACCCCAAALeFxg/fpxWYqR4tKODB5Nl8uT/ehRHQkKCrfW0R5E4ubFpbXL33Xc5GZpgCPhWgAoQ33qzNwQQQOAYgYKCAtm/P1maNj1mlcsLYmNFGjcW+f33P13elg0QQAABfxBYsGCp3HnnbP3RmOFSctLSsuTNN7+RBx54zaXtCIwAAggggAACCCCAgFUCpvXHb7/t9Di63Nwcee+99yQnx/1x70wFSFRUlMdpcTaCPXv2yPbt250NTjgEfCZABYjPqNkRAgggULbAnj1/6wDm1n0dt21bIJs37y57ZyxFAAEE/FjgwIFDti6vUlPda+5vWovMnLlQfvklyY9zSdIQQAABBBBAAAEEglVg/vx3bV1YWZG//Px8+eGHpR5F1aVLV4+2d2XjjIwMWbrUs/S6sj/CIuCsgHV33JzdI+EQQAABBI4SyMnJE9Nyw6opJqZQnxLJsyo64kEAAQR8JjBx4kxJSXGt5UfpxJntR42aWnoxnxFAAAEEEEAAAQQQ8LqAqQAoKiqyZD9paamyYsUKj+K66aabxAyq7ospLy9PK0AYl88X1uzDNQEqQFzzIjQCQSVgTsp79/4tq1cnyZo1213ubiSoMCoxMzVqJOhgvtZcIJlsJCdH6AVOfCXmiF0jgAACrgukpWXKl1+ulIKCQtc3LrWF6VZw7dodpZbyEQEEEEAAAQQQQAAB7wrs3r3L0h1s3LjRo/i6du2m3W039SgOVzY+ePCgK8EJi4BPBCJ9shd2ggACfiWwcuUWeemld/RJgg1Sv75IgwaiN5xEdui9opycSLnwwtPl2msHSZMmdf0q3cGamDp1qsvBg2G2YxAR4Xku160Ll06dTvA8ImJAAAEEfCjwzTerJTc335I9Jieny7vvfqffhc0tiY9IEEAAAQQQQAABBEJXYNOmjfLKK6/IsmU/yIEDB2wQkZFR0rp1K7ngggtl6NBhYsbbMFNhoXUPN5r4zJihnkxhYWGa9tfkzDP7aktrzwZmdyYdMTExzgQjDAI+FaACxKfc7AyByhX4++9UufHGJ7SFwBYZNy5DnnlGJLxUO7BM7Xb9ww8/lkGDvpbBg8+T228foYNm8VXh7SPXuXNLvZhaKX36eLanffvMMY3TFiD/XHx5FhtbI4AAAr4TWLLkV8nIOGzZDr//fp1lcRERAggggAACCCCAQOgJmAG9r712tOza9XuZlQd//rlffvrpJ3nssUdl5MhRMmnSnVKrVk0xy62aGjdu7HFUTZo0kRkzZsnYsddaNj5JWYkylS1dunQpaxXLEKhUgVK3Pis1LewcAQS8KLBp0y4566xxcs45a+S99zKkX79jKz/M7uO156QRIwq0G5J0SU39SM477yZJT8/yYsqI2giMGTNU5szxvNLi1Vej9cLrYlARQACBgBPYtm2vpWnet+9vS+MjMgQQQAABBBBAAIHQEZg3b56cfXZ/7VZ1TZmVH3aJ7Oxs2/oZM16S3r17Sfv27e2rPH6Ni4vTOHt7HI+JoJ/eBJo37y2pV6+eREdHWxJn6UiqVasmp5zSs/RiPiNQ6QJUgFT6ISABCHhfwIzzMXToHdrtVYq26igQrZSvcDKtFu+7L1suvvh3GTJkksfNLivcYYgH6N27vT753EC++859CH04RT77rKpcccXZ7kfClggggEAlCVg1WKQ9+RaNPWmPjlcEEEAAAQQQQACBEBF444039H7IPdpawvkuo7KysmT79m3y7bffSmJioiVSpjupXr2sqQAxCerZs6f8+OPPcuWVV0nNmjUtS6c9s6ZixeyDCQF/E6ACxN+OCOlBwGIBc0Np3LjJ8sADqdKxo+uRjxiRL23a7JZp0951fWO2cFrANBWdMeMeufvuqrJnj9ObHQmYkSFy3XXx8txzkyQuLvbIct4ggAACgSLQokVDS5PasGEtS+MjMgQQQAABBBBAAIHgF0hKSpIHH3xA0tLSXM5sYWGhdjmeLKZViBVTt27dpHbt2lZEdSSOqlWrypQpT8j69Rtk1qyX5eabb7G1CjH3JDyZ4uLitWeLsRJhxcCmniSEbREoQ4AKkDJQWIRAMAl8/vlPOhjXLu36yv2BuO6+O1tee22BmDFEmLwn0LRpfXn22XvksssSZMsW5/fz558iw4bFyzXXXC2nneZGLZfzuyIkAggg4DWBPn066vmqimXx9+nTwbK4iAgBBBBAAAEEEEAgNASuvfYal1p+lFYxg5ZHRkZKlSqeXdfWqFFDxxaZUjp6yz7HxsZK//5na0uX++WHH5Z7XNHSsGEDueGGGy1LHxEhYKUAFSBWahIXAn4oMH36mzJhgo5s7sGk3U7K5ZcflldfXehBLGzqjMAZZ5wkL788WZ+cqCNPPx2tT52Uv1VOjmgfnhE6YH01Hax+oowadWH5gVmDAAII+LnAmWeepP0RR1qSypo1q2rXj6dbEheRIIAAAggggAACCISGwNq1a7VHBje6ZCjFY7rD8qQSxLTSuP32O6Rp06alYvbOR1PZMmfOK1K9enW3dlCnTh15++13bXl2KwI2QsDLAlSAeBmY6BGoTIHMzGzZu/cP6dTJ81QMGZIvH3202POIiKFCga5dW8vSpbMlJuYSbblTXbswqyozZ4bLV1+JfP21aEVUmNx6a4K29kiUHTvOliVL5sjAgb0qjJcACCCAgD8LVK+eIGed1VnCwz1rfm/yWKdONTn55Jb+nF3ShgACCCCAAAIIIOBnAnPmzHY44LkryY2KirINiG66hnJlMuOHXH31SBk7dpwrm3kctlevXvLKK6+KGcjc2e6wzBgljRo1kk8++VSaN2/ucRqIAAFvCVjzmJ23Uke8CCDgkcDatdstqfwwiahbV/RCIE3y801zzgiP0sXGFQtUqRIjkyZdKbfddrmsXLlFVq3aIsuW/SZmTJf69RtoN1lttbustlK1ahXbxYnVgwdXnEJCIIAAAtYLPPXUWFm8eLUcPOig+VsFuzUVKXPn3l5BKFYjgAACCCCAAAIIIHC0wIoVK45e4MGn7OwcWyXGFu3f+uWXZ0lqaqrt93x5UZoKE1P5Ybq9Gjx4cHnBvLZ89erVMnHibbb4K7q/YMb5MK1ULrpooEye/KiOQ6rdhjAh4McCVID48cEhaQh4KrBr1wE5/njtJ8miqV69cNs4IPXr17QoRqKpSMBUNp1yyom2uaKwrEcAAQQCXcCcX6ZNu067bnxefyS63n2jqRQeOfIc6datdaBTkH4EEPAjgYiIDO2WtK+miA4U/OiwkBQEEEDAcoGUlGTL4szKypQNGzbIvffeJ5deOlSeeeZp+fzzz7S1c7gcPnxYHy7N1+5fo7XnhxjbwOFXXDFCe38YL7Vq1bIsDc5G9MQTU7TXiRk69skhpzYxFSCm5YcZP4TKD6fICFTJAlSAVPIBYPcIeFNAGwtYPlX0JIDlOyRCBBBAAIGQEhg6tK8kJ6fLAw+8qj/CnK8ESUyM06fl+sjjj48OKS8yiwAC3hfIzOymOwnXJ3fDdPb+/tgDAggggEBwCJiKDjOZ7qGef/4FMQOkJyUlyZYtmyUvL9fW4qN9+/bSoEEjp7udslrm0Ucny6xZMyU9Pd3pqHNzc22VO61bt5TGjRvLBRdcaKu8MZUiTAj4owAVIP54VEgTAhYJNGpUS8eMiNbYci2J8cCBQqldu5olcREJAggggAAC5QmMH3+hdOzYTEaMeFy7X8wQM6ZVeVNMTJQ2wY8T033WsGFnVNqPx/LSx3KcaNgvAAA3iUlEQVQEEAgOASo/guM4kgsEEEDAkUB4uHXdfZtxNOqavsRLTKblRJs2bWxzZGT4ke6s8/MLS4Ty3dtly36Q2bNfdqnyo2TqCgsLZdeuXbbWI2+99ab85z+D5L//nSxVqlQpGYz3CFS6AG14K/0QkAAEvCdw0kknyNq1ng8ma1KYrC1Bq1ZNkKgo6k29d8SIGQEEEEDALtCrV3vZuHGurUusNm2Ok5o1q2qXAIlSo0aC7dV8btKkrtx//wh9Am22jo10JpUfdjxeEUAAAQQQQAABBFwWaNasqcvblLeBGUy8c+cu5a2u9OWmNcq4cWNtY5N4mhgTl+k+65133pZevXrKvn37PI2S7RGwVIA7mZZyEhkC/iWQmBivN4xqy6ZN6dK2rWdp++CDSG3WeLpnkbA1AggggAACLgjExkbLVVedbZtzcnL1fLZbn1DLErPcVIqYlh9MCCCAAAIIIIAAAghYITBgwHn6EOk6yc31fCxV0wLk5JNPtiJZXolj0aJFbrf8KC9B2dnZthYhAwacI999970+uFSjvKAsR8CnAlSA+JSbnSHge4Hx44fL9OnPaH+TWW7vXLt3lNdfr6IDdl3kdhxsiAACCCCAgCcCMTHRYlo2MiGAQPAImLHlfvpps7z33heyatVG+euvNG3JJdp1Rox06nSCnHvuGXLRRafSAjl4Djk5QQABBPxa4Morr5LnnnvOkgqQvn3PkISEBL/N74wZ0yUtLc3y9Jlz+/79++X666+Tt9562/L4iRABdwToAssdNbZBIIAEBg7sJXv3NpD/+z/3Ez11aowOLHuu1K9f0/1I2BIBBBBAAAEEEEAAgWKBVau2Sp8+o+Wxx+6Xrl0Xycsv75YVK1Jl+fJU+eijA3LJJctl8eJp+vTs5fLGG1/o4OOMPk7hQQABBBDwroBpsTBkyBBtbRzr0Y5MPPfdd79HcXh74y1btnhtF/n5+XpOXy4//PCD1/ZBxAi4ImDN4ACu7JGwCASQQG7uF179peWrQa927twvAwfeJK+9liotW7p2AP73v3D9QXqcDqb+Ik/fuUbnk9C+KkM+yQw78bkA5cfn5EG3Q8pQ0B1Sn2aI8uNTbr/ZmanImDr1Dfnww491jJ8MadfOcdJSUkTuuitOsrNbaovkhyQu7p+bUr4sP9nZbSU/P1ErYRynlbWBJeDLMhRYMqTWGQHKjzNKgRkmJydHevbsIb///rtbGUhIqCqTJk2S66673uH2lVmGsrKypGPH9pJiTrJenHr37q0PNXxi20OtWjW4B+1Fa6J2LEALEMc+rEUgKASaNasvr776X+1DvZp89ZVz5xwdw0qbfkbLjBkNZcGCqVR+BEVJIBMIIIAAAggggEDlCjz66Cvy88//k08/rbjyw6TUdB8+Y0aW9O69US6++HYx4wExIYAAAggg4C2BmJgYvWn/sfaAUV+7ZXTu/ok9LabyY9CgQTJ+/HX2RX75mpeX53Le3MnIr7/+KocPH3ZnU7ZBwFIBKkAs5SQyBPxXoHPnlvpD8wWZM6e1XHNNvPa3LGU+xabnQT3Zh8mAAQmSmtpPK0xe1IHUq/pvxkgZAggggAACCCCAQEAILF68Wr755lOZOTNLoqNdS/Lo0XnSpcvv8sgjc13bkNAIIIAAAgi4KHDccU20F4zF0rlzZ6la1bn7Iabbq4kTJ8ozz0zzSeWCi1k6KnhcXJwUFnq/WWN4eLhs2LDhqH3zAYHKEHCtKrMyUsg+Q16gRYsWPfVL8watee+lTebrKUi2zqazwgXaL+Pz69aty/QWUrB0gVXSx3Q78M03q+Wll96SzZt/1y6xwqRBg0I9+Yls3x4uf/wh0q9fNxk79lI58cTjS27Kez8UqMxms37IQZJcFKD8uAhG8GMEKEPHkLDABQHKjwtYQRA0Ly9funW7SsfzOCjNmrmXIfOgznnnJWj3rFPlpJOa224wmWvb/Hy9kPXiRBdYXsStxKj5DqpE/CDYNeUnCA6iE1kw55j589/VrhuflEOHDonpOio729yS+mdKTKwm5iZ/nz695f77H5TmzZvbV1X4Wtll6KSTOsnu3bsqTKcnAcxYKtOmPavjqlwqdIHliSTbeioQ6WkEbI+ANwVatWp1t8Y/2exDTzw5+vKbzqb6vbuZ9cQzUitIztq2bdse/czkhIBpwtmvX2fbXKD9XG3Zskf+/DNFIiLC9cdoA2ncuLbfP63gRDYJggACCCCAAAIIIOBHAh9/vEx69TrsduWHyUpUlMitt2bIiy++pZUg9/pR7kgKAggggEAwCpj7J0OHDrPNSUlJsnr1L7JmzRpbJYjpIqtbt27aOrGLmIqQQJvOOKOvPpTwhrnX5rWkF+qTtmZmQqCyBWgBUtlHgP2XK9C6deuL9Iv4IxNAX6dprfr9W7ZsSTefW7Zs2UNPRG/pW1O9vmLr1q299NXyb9VgbAGiTkxBJFDZT40EEWVIZoXyE5KH3dJMU4Ys5Qy5yCg/oXXI//OfW3Qw800VDnpekYoZp65nz6qyceN82xh15sYNLUAqUmN9WQJ8B5WlwjJnBSg/zkoRrjyByi5DpkJnwIBzvDoQerVq1WT27Dly5pln0QKkvILAcp8IMAaIT5jZiTsCWks81WynFR2f6BfzLfbKD7NMP/+o6y/Rt6aq+hRtKTLELGdCAAEEEEAAAQQQQAAB/xIwlRRJSXu1e1XP0xURIRpPmFaA/O55ZMSAAAIIIIBAiArog8XSvXsPiYz0XudAEXrS7tTppBAVJtv+JEAFiD8dDdJyREArNHprxUdLs0B/MD15ZEWJN9rt1Wr9uLh40dUlVvEWAQQQQAABBBBAAAEE/EQgJSVDdGxYfbDJmgS1bp0jmzZ5t99ya1JKLAgggAACCPivwIsvTpeaNWt6LYHVq1fXlh+1vBY/ESPgrID3qvmcTQHhEChDQCs/zjBPiumU0bBhw+XaxVUZoWytQ77ScGdp+NM0gKnQs7wbrDJ3zEIE/FDg0KEM+eKLn2XVqrWyc+duW1+e9evXkc6dO8nZZ3eT446r44epJkkIIIAAAgggEOwCubl5EhNjUe2HYsXGFkpOjo6IzoQAAggggAACbgvU0KcT3n//Axk06D9y8OBBt+Mpa8O4uDiZMOHGslaxDAGfC1AB4nNyduiMgHZv1VErNUzQpO+++y7fwTabzTqtBInT5nuttWusTQ7CsgqBoBT4669DcvfdL8iSJavk/PPzpEePHBk2TGsEtUpw717RQdp+kEsuqaID3B8v//3vjdK2bZOgdCBTCCCAAAIIIOCfAomJcZKaat1zSikpkdqiJMEnmc3ObisFBVV9si92ggACCCCAgK8F2rVrL59/vkiuuGK43j/YK5mZmZYkoW7dunLZZZdZEheRIOCpABUgngqyvVcEtPKjsYlYX/c42oFZX9xSxAQz21AB4giMdUEnsHDhcrnjjqfl5pvT5bHHCsX0i11y0m49pW/ffLnppnT54Ydf5fLLb5WrrrpEbrxxqPn/KhmU9wgggAACCCCAgFcE4uJitRIhWrKzTesNz3exdm2UXv+08DyiCmLIzOyqIcL1YaswnSsI7KerU1JSZPXqX3TMlLV6U+uQxMVV04dhOsrJJ3emWxI/PWYkCwEEEPC1wAknnKAPVP4gc+fOkRdeeEGysjI9GhzddH316quvS3R0tK+zwv4QKFOACpAyWVjoBwK2x6y0ciPLUVoKCgqyStzEtfzRrMhIbw+TY78BHaYDT3l7X44kWReIAu+887U888x0+d//MkQfrnA4mbqO3r1FFi3K0MqP+dq8NVUefXS8w21YGQoCfAeFwlH2bh4pQ971DfbYKT/BfoRL5q9Xr46yePG3ct55JZe6/j45WSQ5OUoaNbJ37ent6+gwfcAk8K7T16xZI88++6Bs2bJGTj21QAeOT9PWwNq/cIbIN98kaqvgCGnevIM+JPOgdOnSxfUDEQRb2H9Hmld+iwXBAfVxFig/PgYPwt35WxmKjIyW664bL+PHj9NK843y9ddfy5QpU1xuEWIqP2bNmqUV7Z2C8KiRpUAVoAIkUI9c8Ke7SnEWcx1lVU8YOfb14eHhcfb3Vr3aT0hWxVdePP88iG+/CVBeKJYj8K/Ar7/u1BYf0+WjjzJEry+cnqrof9aMGVly5ZVfyvz5bWTo0DOd3paAwSvAd1DwHltf5Ywy5Cvp4NwP5Sc4j2vpXI0ff6nceutKrQBJL73Kpc/z5kVpa9YLtavPf66dfVF+fPWbwBGEafW+b98+SU9P11Y0sTq223FaMVOq6a9GkJeXp5Ubd+uTvO/K/fcfkl69yhp8Ps3WomXFiu/lzjsvlu7dB8kDDzyh47TEOEpCUK/zh2Mc1MBBnjnKT5AfYB9kz5/KkElL+/btbfOIESNk9OjRsnLlSu3KMtWhhBnzw3R79cYbb0inTlR+OMRipc8FqADxOTk7dEbAtPwoPgE4bC+nY4XEasWHLUp977C1iDP7LR2mRPdapVdZ9DlMuyEyY5iY6AK0Xb1FEkTjvICWdb0IeVhbf7hW+WHfg/mtPG1apgwcOF3OPLOz1K5dzb6K15AT4Dso5A655RmmDFlOGlIRUn5C6XB36nSCJCY2l08+WS8XXujeeCB79oi8+26CLFs20Hb97KvraO//Jii7JGhrd22t8Y08/fTTsnnzZq30+ed3j/mdZNY1bNhQrr/+ehk8eLCtUiQ3N1dGjBgo7dqtls8+O6ytGsqO1yw1dj17inz66SF56qm3dfy49fLWW5/a4il/q+BaU/KGY2Ud4+ASDa3cUH5C63h7I7eBUIZq1qwpH374oXz77be21iCbNm2ydaWdlZUl5r5EFX3CMlJPNomJidrTxI16Dhph6/aK71RvlBji9ESAR8490WNbrwm0bt16mX5h6iW5LNy6deuF5e2oVatW3XXdj8Xr+2vYr8sL687y3NwvvForYZpam5OeOTnk57v3Q9CdfLFNYAt8+eVKmTfvMZk+3bPByWbMiNCLlmHah/aIwAYh9W4L8B3kNh0bFgtQhigKnghQfjzRC8xt9+9Plv79x8ubb6Zq90uu5cGMH3LppfEyceId2oqkh63LIm9fR5sxQIqKIoofVnItvZ6G3rx5k7Z0uVIOHDggaWlp5UYXHx8vCQkJel34klYuvSUNGiyU229XLBen55+P0UqWftr3+zwXtwzc4HwHBe6x84eUU3784SgEdhoCsQyZAdI3bPhVdu7cqRXxhVKvXl3p2LGT1Klj75ay/GNSq1YN7kGXz8MaLwv88wiJl3dC9Ai4KqAVAr8Xb2MGNi930h89TUus3FHiPW8RCFqB2bPny8iRnlV+GJzLLivQJ/0+D1onMoYAAggggAAC/iVQv35NmTPnIbniiqqybp3zaTt4UGT48HgZNGiorfLD+S0DM6R52vb888+Tbdu2Oaz8MLkzN6P+/PNP7d70Ctm06ROtIHK98sPEM2FCjvz991IdL+4z85EJAQQQQACBYwRMpXv37j20K+1hel4eLmed1c+pyo9jImIBAj4WoALEx+DszmmBtSakVnC0ateunaNusDoWx3hIW39QAeI0LwEDVcC0Ftqw4TcdrNLzHFTTnq9q1MjX/qT1rgITAggggAACCCDgA4Hu3dtoC5AndDyQBjJ5cowOaF7+TrVHJ+3yKkwuuCBRrr32ernhhkvLDxwka8ygsxMn3iqHDh1yKUfR0VnaeiPH1rWVSxsWBzZdYk2dmipPPHG3rXW6O3GwDQIIIIAAAggg4I8CDnoF9cfkkqZQEdC+BBdpH7eP6c3eOB3I7zTNd5ldW+n6c4tNeIw9VApHiOfzr79SdcwO0T6grYFo375A1q/fqX1I17ImQmJBAAEEEEAAAQQqEOjQoZl8//0s7bbpQ7nooo+kVat8fbgjU1q0KLBd4/zxR5isXRsvS5eGydlnnyJffTVSTOuRYJ9SUlJ0TI/xFQ40W5ZDs2YiJ5xQ1hrnl+m46tKoUZptsNtu3bo5vyEhEUAAAQQQQAABPxagAsSPD04oJ02be6/W8T1Wq8HJOt+h8zEVILq+ny63PQevLUVm63smBIJeICPjsA4wpo/oWTQlJhaIiZMJAQQQQAABBBDwpUB0dJTcfPNQHTR1iKxevU1vum+Rn37aYRtUtW7detq9Rlsd/LutxMfH+jJZlbqvBx+83+WWHybBZrDzYcOsSfq556bIkiVfCRUg1ngSCwIIIIAAAghUvgAVIJV/DEhBOQLaCuQWbQXyra7u37Jly+na2uNOrRixjQColR9n6fJ5xZsu2LJly+JyomExAkElEBcXoxUWRZblKTMzQkycTAgggAACCCCAQGUI6PW+tv5oZZsrY//+ss+MjAz59NNPJT8/3+UkVa2qT42Zx8YsmEw8jz/+gwUxEQUCCCCAAAIIIOAfAhZ1ouIfmSEVwSWglR3/p5Ue4zVXhdrCY7z+ODqgFR+bdf5Dl5kWIfV0/kErSq7WVyYEQkKgXr0aOtBlkfbNbE12f/01Qtq31z4TmBBAAAEEEEAAAQQqTeD7778X7frXrf2brlHr1HFr02M2Ml2tHjrkYGCWY7ZgAQIIIIAAAggg4N8CVID49/EJ+dQlJSXN1MoP8zzTazrv19ncqY3RZf+nr2N04PPT7a1C9DMTAkEvoGVfWrZsLL/+6nlW9UFD2b8/XBo31l+6TAgggAACCCCAAAKVJrB8+TJt5asXZ25OZsB4KyYTT2RklBVREQcCCCCAAAIIIOAXAnSB5ReHgUQ4EtDurdbp+qsdhWEdAqEkMHLkJfLKK09pv9hZHmX7/fcjZMiQs8RUqjAhgAACCCCAAAIIVJ7A1q1b3N55To7IFt28RQu3oziy4ebNog/bnHjkM28QQAABBBBAAIFAF6AFSKAfQdKPAAIhJ3DBBafoYKEJsmmT+1lP09F0Zs2KkzFjLnY/ErZEAAEEEEAAAQQQsERAu/51Ox7TcORr00GwBdOPP8bqeCJ9LYiJKBBAAAEEEEAAAf8QoALEP44DqUAAAQScFoiMjJDp0++RG29MkCw3GoGY39e33x4nN910pTRoUMvp/RIQAQQQQAABBBBAwDsCxx/f1KOIFy4U8bQbLDP++scfV5EBA87zKC1sjAACCCCAAAII+JMAFSD+dDRICwIIIOCkQLdureXqqy+Xyy+PF9Oaw9nJ/LC9444qUq1adxk16gJnNyMcAggggAACCCCAgBcFevXqJXFxcW7vITtb5KWX3N7ctuHrr0fJGWcMkMTERM8iYmsEEEAAAQQQQMCPBKgA8aODQVIQQAABVwTGjh2klSBj9Cm9qvLNNyIV9ZywcaPIBRckSO3aZ8sLL9zB2B+uYBMWAQQQQAABBBDwosBpp50u0dExbu8hM1PkuedEkpLci+K330Rmz64pd931mHsRsBUCCCCAAAIIIOCnAox866cHhmT5h0Bu7hfud8brRBYiI8NtN6FNn7/5+YVObEEQBI4WMGXo99//lFtumaqvO+SSSzKlS5dCOeEEkXCt4t67V3S8EJH//a+q5OTUkClTbpEePdoeHQmfQlaA76CQPfSWZZwyZBllSEZE+QnJw25Zpr1ZfiIj0yQ1tZ8trUVFYRU+ZGJVpq644nL54ovPdX/u/wSpX1/ks89EmjZ1PlV79ogMG1Zdpk59S68Tezq/YYCH9GYZCnAaku+EAOXHCSSCOBQItTJUq1YN7kE7LBGsRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwN4Ewf0sQ6UEgFARatGjRMzw8/IawsLBeRUVF9TTP2Tpv0XlBbGzs8+vWrcsMBQfy6J5Ay5YtL9Ytr9Ly001fa+uco/NOLUvfFBYWvrh9+/Zt+pkJAacFmjZtWj06OnqDbtBQ59+3bt3a1OmNCRhyAn379o3cu3fvaP0OukIz31bnKjrv08/f6/fQDC0/P4UcChl2ViCiVatWwzXwZTp31rmmzuYaaJfO3+o57KVt27Zt1PdMIS7QpUuXqLS0tPv0e+VupYjQ+SH9bnnQEUu7du0S8vLyJmiY/+jcWuc4nQ/ovFTL1rNatlboe6YQEXCnDOn3UyPluUHnc3VurnOszsk6/6Lz21oG39TXQp2ZglzAnfJTFknr1q3v0GujKWadvo5MSkp6taxwLAsuAXfLj94naqf3iW5Sjf46N9A5Vc+D2/Uc9k6VKlXmcJ8ouMoJufGtQLhvd8feEEBAL6zv1pPaMpW4TC+CTOXHbzqbCo/uOj+WnZ39i574Gut7JgSOEmjYsGGclp/P9CLoA50v0pWm8uN3nfN17qDLbo6IiFivF9pD9DMTAk4LaOXHcxrYVH4wIeBQQG8w1ty3b98y/b55SQP20jlL5306NzU/7PV1hX5PXa+vTAgcJWDKjp6flujC13UeoLO5sZik8986n6jzBL0+WqNhxut7phAW0O+QNunp6cv1e+Y+ZTCVHxVOzZs3b6KVH6s14GM699DZVKzt0NlcKw3TsrVcy9Ykfc8UAgLulCEtH+baepPOppx00jlX5906mzJkvrNe1zCLO3bsGK/vmYJYwJ3yUxaHPrTWVq+NHi5rHcuCV8Dd8qPfL9foucpUtl6r57+6+rpT50gtQz3187N6n+gnjdt8HzEhgIAbAlSAuIHGJgi4K1B8YT3ZbK8nsml6IqujTxK10bmRfj5FF5sfaq30xPeevvL/qQhM/wokJCTM0U/mB5gWl6J7s7KyqmnZaalzDV12ms6m/MTqutfbtm17vL5nQqBCgeLvpREasKDCwAQIdYEwvcG4QBFM67M1+l1zsn7/HKdzC618PU6XfaSzaV08zTzBpq9MCBwRyM3NfUXLTE9dYG4qjtFyU1Pndjo31+sh8+DHZzpHaZgX9aaRuYHNFHoCYXrszdP35gZQF50/d5IgPDIy8gMN20Jn82BIHy1XDXRuqy2ra2uZesLEo6+P680jcx3FFLwCbpWhNm3adNDyYX5/VdV5tX4nddPyU1vnE/S9uc5+2pBpmNNzcnKmmvdMQSngVvkpRyJCy85rui5GZ66xy0EKssVulx899/XX75dZ6hGp8136EIC5T9RW5zpaji7VZek6m4dFnteZCQEE3BDgBqsbaGyCgLsC2nTRdsGsJ7FPtPnrLVu2bDEnMtukn3/U9ZfohyKdT9EfaDzF/w8Nf1Wg+GbisGKMKVpeJu/Zs+ewHUcvjpboRdPw4s+xBQUF5klsJgQcCpgnsrXczDSB9HvpdYeBWRnyAlpZZr5jTGXrHzr31++hNXaUTZs2/aGVsqZbo690fkcrROrZ1/GKgN5cbKrfMebpavNd85yes17Wt4V2Gb0eMl2oDdPZtCjSlzBagdhxQuhVbwCdr8fetEgM19ebtZyc70z29bvJfPd01blIHyIarNsttW9nugvR76pJ+tlU0JrpqX9e+BuMAu6WIb1uvl89onVO0fPX+fqdtNLuY36vaZm6TT9/YZbpddNVjRs3rmJfz2vwCLhbfsoS0N/y5nvHPDDyrc57ygrDsuAScLf8mK5l7ec+/X65X79vHtfW1uZ6yEyF+h30nq6/Rd+b76C9OnMfVxGYEHBVgH8cV8UIj4CbAnoR1FtPXC3N5npie7KsaLRvYtN0f3HxuqvLCsOy0BTQH/RdNOfJOhdpOTJPhxwzmUo0XWiefDTTSf+88BeB8gX0af4XdG19/U56Xytgvy8/JGsQsJ27zJPZZnpSf5yZbouOmkylrC4/W+cR+mPNfi47KgwfQlagtT3neqOxzO8ac5NRv4u2FIc7Et6+Ha/BL6DXN+bJ14362l3Lw7P63jwUVOGk5cb+0Me3mzdvXlXOBvan9tvqTaoe5YRhcYALuFuGdLtWmvUcnT8xFfplMWg5e7d4uXbFX4XvqLKQAnyZu+WndLa1JX57XfaAzvlabm4tvZ7PwSngbvn5448/+qlIG50PaHmxn6uOQtJz4hy9vh6g80RdceQBkqMC8QEBBBwKUAHikIeVCFgnoCfEM4pjy9CxHJaXF7OGM0/PmscfT9MX/kfLgwqx5Xqx87rOtapWrRqjF0A7HWQ/r3hdjIMwrEJA9AbQxcpgnpo9qJUf10OCgCMBrcRvpOu7F4eZ7ygs6xAoLaCVHn/al+nT1WbsjzIn/eEfVbyizBuQZW7EwmAS+FlbknXV65x1zmbKPDmrYXsXh7ddQ5e1rV57r9DlGWadXmOfWVYYlgWFgMtlyORar7E76Ryr19mjy1PQh5Hs19gmCNfZ5UEF9nK3yk/JLJvvJL2ufk2XRet3zeMlW8uWDMf7oBRwq/zotY/5TWamj/WBWFMRy4QAAl4QMBeMTAgg4AMBvRDqqBdBZk9J3333nRm0urxps1mhJ8I4vUHZWi+aNpUXkOWhJ7Bq1aqSP76OAtAblLV1QdPihRuPWskHBEoIFJeVl8wi/a65Yfv27Qf0+6ZECN4icLSAnr+6alkxJ7EDepNor3bL11hvBo3Szz11rqnrzQ3ub3Seqzcvj3TvqJ+ZEJDExMQN2p+1qbxvpuXIdKVm+to/ajrhhBOO03JknoA0N6gXHrWSDyEhYL5bXM2otjxrrd9FtpvRWm7KvfYx19567tum8ZsWsrSSdRU6QMK7U4ZKZs3Rdbb+luuqZcwEL9CxZbaW3I73wSHgafkxCtp10d360lnn9To20SNmGVNoCHhQfkxXaebax4x/FabdOl6o10qmO/RmOhfo+w368Mg8beG4TD8zIYCAmwJUgLgJx2YIuCqgJzQzwKc5se1xtK1Zryc5exCzDRUgdg1eHQpo2ZmkZcd8r+fr+zK7yXIYAStDSWC6Zrauzgu0kvXtUMo4eXVPQL9b2pktzTlKf5gN1M9v6EczWKxtKj5vXagf7tSbjAP1R+BPxat4QUDMTUUtF2OU4mOd/6MVrtP1x/y0+Pj4nRkZGbFark7Vm4tmkGFzDvtaK9Fe01cmBCoU0BuMjbXs2MJpSyOH19gayKw3lR+2a3LbRvxBwAkBPe811PPcyOKgb61fvz7Fic0IEmICep7rpFm+V+d8rZgduWHDhtwQIyC7rguYWtW2ZjP9jknWMvSpvg4oGY1eI52m57nxuu5lvb4er+sKSq7nPQIIOCdA9zrOOREKASsEbDeK9ISW5Sgy/fFWcv2Rm0uOtmEdAnpBNEjL1q3FEs/pzaMtqCBQloD+iL9Ul5unig7qGCDmIpoJAWcEahUHqq3fNW/q++91Nj/IqumP/Nr64+wa/XxQ5/o6L9T+rxvoKxMCRwT0R/vXWl566wIzmOdYfb9FW4XkanlK0/dmYM84ne+Oioo6X1/5ca8ITBULaPk5cq2s30Ulr6HL2ti+/sg2ZQViGQIlBcyA51rOTKu1ajqn6veVecKfCYGjBLp06WK6cDSV91F6jpviYDyio7bjQ2gL6O+yBBWwd6n3oL43Lasn6HyCftfE6nfPyfr+Q53NdK2Gf+ift/xFAAFXBcxTVkwIIOAbgSrFu3H4JIheMOXYk6M/5MzNACYEHApo5ccIDTBHZ1Op/YX2cz1JbzQ53IaVoSmgXczU1QvpF03u9fXGnTt3/hmaEuTaDQHbDUMtN01020/1O+ZCfT3SXFHfz9XvovX6ulznOlqZf7u+2itl9S0TAnqSCg/vomWorV7rmPOV6SrNPJFvxgRpqnNDnXtpxexn+rpWZyYEKhTQ8mS/vjZhHV5j63r7NTbX1xXKEsAItGnTppbehPxI356qc65+d12sffRX1NLIbMoUYgJaoW9afnTS76RftSL/4RDLPtl1X6BkhXwr/b7prd8x5lraPq3RN5foNfYCfTUtsG/T33PPme6L7QF4RQAB5wTMjw8mBBDwgYCerOxPnUU72p2p6bev1/f2beyLeEXgKAF9CuQ+XfC6zuZpoy+1T+JLKhhj5qjt+RBaAtpVyHTNsRkr5iPt+uqt0Mo9ufVQ4Ehlh56b/qtxHflsj1crRX7W95+bz/p9NNi+nFcEjICer8y4Q7O0bDTU+VItLzV0PlHn5lphZloOzdbZtP5YoTcd++orEwIVCpS4vjZhHV5j63r7NTbX1xXKEkBvMrbQ890yleil82H93hqiDawXI4NAaQEdF808pX+3zgVa0U/XV6WB+FyugJaXI9fTej77qlTlh327Ig1nH08mVt+bayUmBBBwUYAKEBfBCI6AuwJ6ojJPOpop/p+Xsv9qONMM0jbphXaa/T2vCJQU0AvtGO1DfZ5eKNmfMHo1ISHhgnXr1mWWDMd7BOwCWl4u0/Jibkon65Np4+zLeUXASQH7OUyys7PLfTpfz1tLTHxa1pro91Sik3ETLMgF9MnFflombN87WkbG603E9zTLR7q5Mk8yakXI9brsfzrH6k1HU1nL7xRFYHIsoOXqyHeTXkM7vMbWmOzX2FxfO2YN+bV6/jpdxylaoRCtdP5Lv5PO0u+tj0MeBoBjBNq1axet3z2m6yvTu8oTWk5WHhOIBQiUI1DiHpEJUe71tXaptlrX237n63VUh3KiYzECCDgQ4IeFAxxWIWClgP5A+704vsaO4tUTWtMS63eUeM9bBGwCzZs3N33uL9aycrkuKNSydbveOBppBpmFCIGyBJo1a1ZPy8vzuq5IX8fqoIz7ywrHMgTKE9Bys7u8dSWX6/dRsv2z3jyy32y0L+I1dAVMV41mStebQ/P/eVvmX3s/1231BqQZTJYJAYcC+j1jv742Fa8Or7E1oqbFkXF97VA1tFfqAyPD9Tr7K1WopfM6fd+9nKeyQxuK3NsEtNvGB/SNuSG9Sh8wetC2kD8IOCmgv8kyNOghJ4IXlgjH9bUTYARBoLSAqaVmQgAB3wiYGv1hehOplXlSRE925fVT3LE4OYf0pjY/0HxzbAJmLzq+R5x2Y/SpJtj0RZyhP/Yv066MFgZMBkhopQjoD7Jzdcfmh7y5QfSePo3tKB3H63p7c+zX9HvoakeBWRcyAuvtOdWu9prp+432zyVftXxV1/OcbZH2h+3MD7qSm/M+eAUaFGftjwqy+Ld9vd50NNuYJx6ZEChXQCvUkrR7tSz97onTp/Tba8CPygpsrp90+QlmnX5H/VJWGJYhoJUfF2v5eF0lInReqNdPlxXfoAQHgfIEhhev6KKVITmOrrG1bL2i618x4fUc10yf6v+teFteQlvAXGP30fJhrq8dTdXMSj3fpTgKxDoEEChbgBYgZbuwFAHLBfRH2SITqfmBphdHp5W3A11vblSaydaP+j9v+YuASN++fSO1myvzw970RZyiZer0/2/v3kKtKuI4AGuWBkJXIUnQU/ogVmYYREgUZVlSVgT1UnShUrpRPVRvSdCbXegh7J6QBhEVFSF0M8SEXnqQegiSk4SnKxVSopn0+2/XkmN49rmYgu5vYM6stWb23md/DDOz1sxa2+SHmjESgbQrdXfQH8PE7c171eRHp2xe5znpDUqvJ/mNhg0x6Dw2Jift13fxOK/J+2br1q3qTxeoHstqT9an53sPef6RNqfyOyF93N67idpjUgL7EUhV2f1RHc/Foyv2k985NHny5Iuz0fmNkJT7YKhyjveuQO46uyx14/UITEhb9HIWgFxt8qN368Movvm2lB1ujN0uLKqxdqfszp07d4/iMxQ9sgXa6z6Lhnp8bI6fEYL2zo/6YXSBAIFRCrgDZJRgihMYq0Bunf4yKz5qJeM5iQ8ldk7WBr9f8hdmf34dywD8xcF5tgkMDAw8GoWFqRu10vHy1CkrGFWLEQk0P3i+plvhrHq8JXXrlZTZkpP+vm5l5fWeQNqbHVllvSZtz7J8+7v7+vqe7u/v3+cOj+SflvyrGp23ek/JN+4i8FnyauLs2Ix1lqSNeWeIstc2x+uREDVmEggMK5BJ2ZfS9ixJwQVphxbkrpCasB0c0r2Nr7F3hfVZdf3Nnk1/CewRyN35U7NAbXX2apLsjYyb7kjqAvUeHn+7CKQ/m9slu5OVfq8/GzPSTt2VuvVq56A/BBqBXbt2rc4THpZn97j0VfcmfTxxn5B+7r46kPy/Jk2atHafTDsECIxIYMgVWCN6tUIECIxKICvUHsgLagXIpbnY+OzgGf4MjC7J8deaN3w7J2+fNNsSAuNSP07PoPmRokj6cAbbX2AhQIDAoRTIyddj+bxayT9l4sSJa2fOnDmr/fz0afPSNtXj+SYl/pwLSU+3eVICeWzaqih830i8lPpyXbbrETOdMHv27JPTzz2TE/tFzaEVNenWbEsIdBXImLl+nLozbk479EbG1xe2L8hk7QmpWy9k/4LEfzIWv7/NkxJoBbIavy44TkkbtCXt1W3ZNvnR4kgJEDioAps3b96SD3iyPiRt0PL0WffMnz//mNpPH1YLR2oR5J21nz5uxaZNm9q7auuQQIDACAXGj7CcYgQI/E8COelfmo7t2bxdTUDWyX1/4vGJUxMrbMjJ2eKc+HceNbLnkL+9LpB681TqTXvS/lU8dg1nkkmSecOVkU+gFUgda+8A+S51p689LiUwWCAXFs/PRMh7OXZyYk3of5u2aWJOyKY35X5L/pVZYf15sy8h0BHIJMdZGd/Uheq+hmRb0poUqRXXpyXWuKjCi2mDliZ1AbLD0Tt/cpHng3zbU//zjc9u9n9M+sPgvLQ9izP5sbWO5bVTknyY2I59BrJdY+mqW1XH/k47dXNWX7+ebeEIFRhLHUo92pG2qerW0Ym/JraTtUMqpS6tTF1aOWQBGYelwFjqT9sGdfvCed/+5M9IvbnVHSDdpA7vvAOsP0fl9asicGOj8HvSgbRPVW/qN6xq8mP1tGnTblm3bt2w1wGa95AQIDBIoDp5gQCBQyiQQc9zuT1/YzqwB/OxFyXWidmf6dw+q04tJ/0vZ/+fRIHAXoHUjxP37owbV88AFQgQIHDIBTI5vzF3fsyZMGFC9WH1uKsZieMTv04f9n4uIj2Rfuyn7AsE9hHIpNimuXPnnrl9+/bb0qddk8z6wepZiTsTNyfWpFlNfqxPKvSmwJx87WpT9hdOycGKe0PqUU1sdELqzS95jNF5Wcm/LMdvyMF6r5MS62L2x5mYXeHRV5E48sNY6lDVo/a6SE3uV+waUsfahWtdy8k87ATGUn8Ouy/pHz5oAgdSf3anH7sp14nezHj69vyH5ybOynbd7fFp2pznk/9uriUdtH/eGxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA/ynwLwrLMytG3fdGAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At zero threshold, precision: 0.22, recall: 0.74\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from adspy_shared_utilities import plot_class_regions_for_classifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# create a two-feature input vector matching the example plot above\n",
    "jitter_delta = 0.25\n",
    "X_twovar_train = X_train[:,[20,59]]+ np.random.rand(X_train.shape[0], 2) - jitter_delta\n",
    "X_twovar_test  = X_test[:,[20,59]] + np.random.rand(X_test.shape[0], 2) - jitter_delta\n",
    "\n",
    "clf = SVC(kernel='linear', class_weight='balanced').fit(X_twovar_train, y_train)\n",
    "\n",
    "y_scores = clf.decision_function(X_twovar_test)\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_scores)\n",
    "closest_zero = np.argmin(np.abs(thresholds))\n",
    "closest_zero_p = precision[closest_zero]\n",
    "closest_zero_r = recall[closest_zero]\n",
    "\n",
    "plot_class_regions_for_classifier(clf, X_twovar_test, y_test)\n",
    "plt.title(\"SVC, class_weight = 'balanced', optimized for accuracy\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([0.0, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.title (\"Precision-recall curve: SVC, class_weight = 'balanced'\")\n",
    "plt.plot(precision, recall, label = 'Precision-Recall Curve')\n",
    "plt.plot(closest_zero_p, closest_zero_r, 'o', markersize=12, fillstyle='none', c='r', mew=3)\n",
    "plt.xlabel('Precision', fontsize=16)\n",
    "plt.ylabel('Recall', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()\n",
    "print('At zero threshold, precision: {:.2f}, recall: {:.2f}'\n",
    "      .format(closest_zero_p, closest_zero_r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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